Not sure what the big fuzz about low rating of this course is about it. I liked this course: professors explains things well, their points are spot on (in most of the courses in GaTech, professors' english is not even comprehendible.) You are going to learn more than a few things, even though one may thing it's obvious: e.g. how to conduct a poll, corporate data manipulations etc, how to mitigate bias etc. Case in point, by forcing people enter their gatech email in order to post reviews, this brings bias to all of these reviews.

While personally don't agree with the DEI or the need of mitigation, but during the course I left the politics out and I focused to learn the topics, and what AI is about. Also, for those who need more depth, the professors give links to materials of how mathematically bias algorithms are implemented.

Glad I took this course.

PS: Exam1 was a bit a black box one, but other than that I enjoyed this course. Exam2 challenge was where to get the data for corps which abuse with AI. Took me 3 days to get find the data, but that journey was priceless: forcing me to explore diff gov sites for data, filter and link them. Well, just happened that UnitedHealthcare CEO was murdered and his company was sued for misuse of AI.

The course is fairly straight forward. The material is easy to understand especially if you have worked with ML previously. The group project is not over demanding. The final exam was changed at the last minute to be a last assignment. Some of the assignment descriptions could use better wording as it becomes ambiguous what the directions are asking the student to do. There were a few assignments where I lost points including the "final exam" for what I believe to be misunderstandings by the T.A.. The few times I tried to reach out for clarification or rectifying the situation, I was met with silence and so I took the small amount of points lost but earned an A overall.

As others have said, this is a statistics course and very little is taught about actual AI. This class should be more appropriately called 'ethical societal discrepancies' or something like that as all lectures focused on issues in how datasets could be misconstrued. That being said, the lectures are very informative and easy to follow and have everything you need to succeed on exams.

Almost all of the assignments consist of downloading datasets thousands of rows long and finding ways to correlate data. This can be done with coding or this can be done with excel manipulation. Very straightforward and pretty easy. But pretty important as finding correlations between variables is the bread and butter of any data scientist.

Informative, valuable, and easy course.

p.s. Don't listen to the whiny comments complaining about the content. Its literally not that bad and they're being toddlers.

This class is as easy as everyone says it is, so it's good if you're pairing with another course or need something easy because life is otherwise busy.

Here's the rub, though - it actually has something important to say, and I would recommend that anyone in the ML specialty take this course. Specifically, this course teaches one thing: the bias/variance tradeoff can have dire, real-world consequences, and you need to think about how to navigate this tradeoff, or change your models so that you modify how bias is shaped.

In a vacuum, this seems like a really straightforward statement, but when you're inundated with tons of bad statistics, bad applications of data, bad applications of models, and bad modeling in general, it makes you reconsider your approach.

Similar to how ML4T is not really about trading, this class isn't really about ethics, but rather how ML models can be difficult to wrangle even in the best of circumstances, and being a good technologist/data scientist means understanding the nuances of the real-world implications of your models.

So, yeah, it's easy, but the core of the idea of the course is really important, and lesson really only becomes clear after you've been beaten over the head with it a number of times.

I strongly don't recommend this class. Most people (including me) take it because it's an easy class with a somewhat interesting subject area and makes for an easy semester/combo. That's correct, but I strongly underestimated the amount of bullshit assignments and the general bottom of the barrel quality of lectures here. If you'd like to learn about AI Ethics, read Weapons of Math Destruction by Cathy O'Niel. It's an interesting airplane read type of book that still covers all the topics at more depth than this entire course's lectures. And you can skip a bunch of bullshit assignments making graphs, writing a paragraph, and then repeating 15 times for blatantly obvious things. If you'd like an easy class that will still teach you something take Computer Networks or Security Operations and Incidence Response (strongly recommend) and just skip this.

This review is for Summer 2024.

First, if you know Python going into this course, the assignment generally take only a few hours per week. Most of the material is intuitive if you have some general common sense (as in: do you think exploiting peoples' data is a good idea or bad idea?).

The grading is very relaxed, and so long as you complete everything being asked this is fairly light course.

Now the not so great: are the assignments fully obvious in what the deliverables are? For an unbelievable number of people in the class, it appears that the answer is consistently a resounding "No!". I don't understand, as it feels pretty straightforward what is being asked; yet, such an opinion evidently puts me in the minority. Or maybe people just enjoy arguing over pedantic details. But nothing has been so overwhelmingly confounding that achieving an A is unobtainable, in my opinion.

Some assignments do tend to feel redundant and arbitrary, but with some basic Python scripting most things are not particularly complex. Yes, there's writing, and yes, this class relies on JDF (use LaTeX!). But there often are no page limits (looking at you HCI or ML, etc.). So the added stress of needing to edit down a document is non-existent here.

My final comment is that I wish all assignments released earlier; as it is, things are time released seemingly arbitrarily causing me to sometimes spend days with nothing going on. This gives a shocking amount of time to spare, which may be a pleasant reprieve from other OMSCS courses. So work ahead as far as you can; it's not a big time commitment---just prepared to be on standby until the next batch of tasks become available. Rinse, repeat, and try to tune out the often asinine posts your classmates will make on Ed throughout the semester.

If you want to excel academically and earn an A while also considering real societal challenges related to AI, this is the class for you.

I deliberately don't rely on comments here to decide which courses to take. You should choose courses that will benefit your current situation, not just for the sake of learning, getting an A, or feeling proud of passing a tough class. I don't think any serious companies should hire any sophisticated classmates who dismisses notions such as bias and ethics as merely leftist ideas.

So yes, this class is quite easy if you already have knowledge of basic stats. The assignments are very straightforward and the tests are fairly easy if you just watch the lectures. Now, some of the material is indeed worth discussing/interesting such as the laws and ethical considerations surrounding deployment of ML/AI. They also introduce you to some actual software tools and methods to mitigate bias and encourage you to think about the trade-offs when using these algorithms and what 'fair/unbiased' even means in some contexts.

I don't necessarily have a problem with a class being easy, my main gripe with this class is that some of the assignments/tests just seem poorly constructed. There were some questions that were clearly just asking the wrong thing or confused about the thing it was asking us to implement. Other questions were clearly going to return useless results, but the instructions from TA's were to just do it anyway and report those useless results. These questions should probably just be removed/edited? I felt that the intro stats material should just be made optional and dig more deeply into the bias/fairness techniques or something like model transparency/explainability. The final felt like a repeat of the final project, just a report of going through a use case of doing bias mitigation.

I'll contrast this with another class which I would also consider pretty easy (though I'd say slightly harder), but for which I gave a great rating: CS7632 Game AI. The assignments in that class are extremely well designed and incredibly fun, I actually cared about putting extra work in even though I didn't need to since the assignments were engaging instead of some....thing....that I would get out of the way in this class. A revamp/clean-up of the assignments to something more engaging/interesting would easily bump this class up to a 4.

This is a pretty mid class. I expected a lot more discussion about the legal framework, ethics, or current events. Instead, it's a basic stats class in disguise. Really don't recommend wasting your time on this course.

Note to admins - you don't have [Spring 2024] as an option!

Spring 2024

A lot of grunt work that doesn't result in learning, but it works if you are trying to relax after some intense courses. Grading is lenient if you follow the jdf, and check all the requirement boxes. Exams are split into midterm, timed MCQs, and Final ,untimed.

This class is tied for third place as my favorite class in the program, including heavy hitters like AI/ML/etc. It is not a programming class, and it will have little utility for someone in a low-level individual contributor role. Instead it touches on issues that are likely to be hit as a team lead or manager in any regulated field, and more broadly any software that affects the public.

The course is fundamentally structured around US law. Professional Engineers have a legal obligation to consider the health and welfare of the public, and while most CS grads won't get a PE license it is still important for management and team leadership to consider the impact of work on society - even if you don't personally care about ethical behavior, the legal ramifications of getting these things wrong could land you and/or your employer in jeopardy.

Having taken engineering ethics at an engineering school, this is a well-designed computer science equivalent: an ethics class that introduces you to the legal and ethical issues that you are likely to face while you are developing computing, AI, ML solutions. I have run into some of these in real life, and it will be nice to have the legal background as a buffer / correction to my general inclination.

I enjoyed the lectures. I did not necessarily love the work in this class, but it was graded easily and didn't take all that much time, so as a way of keeping me on track it did what it needed to do.

A very dreadful class. Not much to teach or learn, and the homework is extremely tedious and repetitive. The content and assignments really need an overhaul.

I voted for Joe Biden in the 2020 election and this class made me decide to vote for Donald Trump this November.

Ok. This class has merit. A lot of people here hate on this class, but it has a great purpose. It is an excellent introduction to AI, Stats, and Data Analysis using Python. If you've not done any of these before, it's a great chance to learn. A lot of the criticism seems to come from right wing leaning people who are afraid the course is 'woke'. I don't think that's fair. Statistically, this class is showing when AI is biased and when it is not. Certainly many of the datasets we analyzed were not biased, and some were. If you want to approach subjects like race in an intelligent way, this class will give you the tools. The fact that some of the datasets don't show any bias or even any statisctical pattern leads to my one critique. It can be frustrating to compute figures that are meaningless. Or graph outcomes that don't follow any pattern. Do not let that discourage you. Just report the results and let them be. That's how real data works. You're not going to get a bad grade because you don't find bias. Sometimes it's not there. The assignments are sometimes a little ambiguous, but the TAs will clarify in Ed Discussion. I'm sure the TAs hate answering the same questions over again, and the course could benefit from updating the assignments a little bit.

My last and worst class in OMSCS. The class discusses misleading graphs, protected class and bias, which could be covered in less than a week at its depth. Instead this is a semester long course, so tons of repetitions just to fill up the time.

This class could've easily won the most tedious assignments award in my entire academic life. The assignments all exhibit an awful pattern of high volume of low value work. For example, in one of the so-called AI/ML assignments, students are asked to calculate mean and standard deviation for a dataset, copy that into a report and format, calculate those for 10% of data, calculate those for 60% of data. Repeat for a protected class, and repeat for all subgroups in the protected class. The calculation in Python is effortless, the time is spent on copying and formatting ~30 sets of values in the report. No one checks the result as long as some numbers are there. Data analysis is minimal, as long as you wrote two sentences.

It's surprising that this class at its current state is considered a graduate level CS course and one of the electives for II specialization. Anyone with basic coding experience, keep your sanity and stay away.

A solid class that introduces AI fairness. The class is fairly loose compared to a hard science class. Most questions are open ended and common sense based. People are nice, and I run into people with a wide range of experience some who has no coding experience and relies on Excel.

If you are woke, you are going to love this class.

This course fine, it has 4 modules:

1. Introduction to AIES
2. Stats (speed up)
3. Fairness in AI/ML
4. Fairness and bias

The course touch upon important aspect of the trendy AI, the danger of using biased dataset. I think the class is fine, maybe people gets frustrated with all the busywork, but this class is good to pair with some heavy classes.

The course starts out pretty interestingly. It gives a good intro to industry problems and stresses the importance of finding solutions. But, it gets kinda unbearable real quick. The reason for this becomes clear at the end of the course – there's simply not enough material to fill a whole semester with relevant topics.

So, what happens is that throughout the course, you deal with tedious calculations and engage in writing that, while intended to represent your opinion, often earns fewer points because you summarize banal concepts in a sentence and not 5. The same situation arises during the midterm, where you encounter questions like 'Do you think this graph is misleading? Why?' Surprisingly, you can actually give a wrong answer for it. The final presents an additional challenge with numerous criteria for the artifact you need to find. This leaves you with no other option but to rely on synthetic data with no real-life relevance.

The projects' instructions lack clarity, the staff responds to questions but often very late. That actually doesn’t make them fix the document. Because why bother when you can leave every student frustrated and allow each TA to decide individually how to assess the homework?

Too much time is spent on topics that shouldn't be part of this course, such as delving into the basics of AI and ML just to 'pad' the syllabus. Meanwhile, a mere 25% of the material focuses on the practical techniques for addressing the core issues. Even this content relies heavily on open-source libraries that are hardly maintained, or projects developed and supported by Google(...?).

In summary, The course manages to convey the importance of the subject but fails to encourage doing research due to exercises that lack depth. In my view, this material is better to be integrated at a basic level into any course touching upon AI/ML. The other option is to make it available to non-CS students only.

While the course is relatively easy, it feels like you're working without gaining much. You may spend only 6 hours instead of 18, but it doesn't impart meaningful knowledge or intellectual challenges.

Loved this class. I know the try hards at GT love to hate on this class and they do have some merit for the hate but this was a solid class.

Pros: Good for data analysis using python. If you are new or experienced, everyone could learn practically examples from this. There are weekly assignments but it's easy for the most part. Read this, reply to this, give your opinion etc...

Cons: The assignments are pretty poorly worded. However, if you have a question, then more than likely someone else has already asked or it is in the document's FAQ sheet. It's really not as bad as others make it out to be.

This course would pair nicely with a more difficult class.

I timetracked my effort in this class. Overall I put in 52 hours over the course of 16 weeks or 3.25 hours per week. From this class, I learned about bias in algorithms, techniques for detecting bias and about protected classes.

If you have limited experience with python, pandas and matplotlib, this might be a good class to take to gain some exposure to those tools and practice. If you just need the course credits or need something on the lighter end, then this course might be a good match.

This is my first/second course in OMSCS, paired with another class. There's very little programming involved. It's more like, can you find bias in these scenarios?

The weekly assignments are pretty easy. Sometimes it's unclear what they want you to do, but there will be several posts on Ed Discussion asking the same question you have, and the TAs do a great job answering. The megathreads for each assignment is posted by the TA several weeks in advance of the due date, with a FAQs PDF.

There are two exams. The first exam is an actual exam. I did not do any of the readings or look at the lecture slides, but still got a perfect score. The second exam is more of an assignment, and it is untimed. At the time of writing, I did not get my score back yet, but by that point I only need a 20% for an A.

There is a group project that requires some Python, but as long as you get into a good team, you should be fine. Some people even prefer to do it on their own. There's one fewer question for you to answer if you decide to do it on your own.

This is a really good class if you don't come from a CS background or if you're new to the program and need to satisfy GT's foundational requirements.

tl;dr This course is an easy A but you shouldn’t take it if you want to learn anything. It’s terribly run and a very frustrating experience. You’ll see this sentiment consistently mentioned below. Listen to it.

This course is my final course in the program, and it looks like I saved the worst for last. I got an easy A in the course, but it was so frustrating and made me dumber. This course is taught at a middle school level with really basic material like mean, median, and mode….seriously?! In a graduate course? And then at other times they’ll get you to do actually bad math like averaging categories (ex - convert this category to 1, this one to 2, this one to 3….now do some math with that like averaging / correlation?). That’s nonsensical and embarrassing anyone signed off on it.

The assignments are poorly written, and you’ll spend more time trying to figure out what they expect rather than actually doing the assignment. Yet there are no office hours, so you’ll post something to Ed and wait for staff to respond. Sometimes that can take days. And then the assignment is really basic like calculating a bunch of values and then putting them in a bulleted list. Oh, but make sure to use JDF because we’re going to pretend like it’s a hard assignment!

The course doesn’t take much time, and you could pair it with another course easily. You can finish all of the lectures in the first month of the course. But if you’re doing this degree to actually learn things, skip this course. It’s a shame they put such a low quality course for such important material. I’m embarrassed this course is part of the GT curriculum in its current state.

This class was boring for me. All of the assignments felt like busy work.

The mid-term is mainly free written text, with most questions asking the student to explain why a graph is misleading. You can make a good points and give an original thought or two, but if you don't say the keywords the grader is looking for, you won't get points on those questions. If the answer is fixed, why not make it multiple choice?

There will be many weeks where you will spend less than an hour of work. When assignments become due, you will spend a decent chunk of time doing them. Not because the assignments are hard, but because the writeups are unorganized and ambiguous.

The Ed Discussions were regularly filled with students asking for clarifications for each step of each assignment, so you're constantly piecing together what has been said in the Ed Discussions threads with the assignment writeup to find out what exactly you need to do. In my opinion, that level of confusion would mean its time to revise the write up.

The assignments involve lots of data massaging, and showing data in different ways. Again, it is not hard. I used Python, Grafana & Postgres SQL for all of the assignments to make them easier. They were all different variations of the same problems.

I don't think I learned anything..

Easy yet the most frustrating course.

• Assessments: Assignments, Weekly Discussions, Final Project, Mid Term (MCQs, Fill in the blanks, T/F), and a final take-home exam (similar to final project)
• Pros: It's an easy A course if you do all the required stuff. The lectures are good (but you might not need them to complete the course because every assignment except the final exam is so basic). Good to pair with a harder course if you want to graduate early, but otherwise, not worth the amount you pay for it.
• Cons: Each and every assignment is poorly written. You need to ask a lot of questions and search Ed Discussions to really understand the assignment requirements. TAs grade your assignment manually, so your score really depends on the TA who is grading you. Some TAs would cut marks for really basic stuff. For eg, if you write that the random variables are x, y,and z, instead of writing there are three random variables, you will lose points.
• Conclusion: Extremely frustrating course, only take it if you just want an easy course and you dont care about learning.

"AI, Ethics, and Society" is a survey course that explores how ethics in big data, or lack thereof, can be hugely impactful to society. In this course, you will absolutely write more papers than code. If your goal is to supplement your 1337-HAX0RR BIG DATA skills or even learn more about industry tooling in that space, you would be sorely disappointed; this isn't the class for that.

On the other hand: if you want a simple course with a light workload to wrap up OMSCS with and/or know more about how to ethically work with and review large data sets, this is a fine course. You could also pair this with another simple or medium difficulty (think 3.5~4 rating on OMSCentral) and not struggle as long as you have good time management. I work full-time, have two kids in school, am taking two courses this Fall, and have been able to keep up with both. Currently have an A in my other course and will likely pass this one with a high B or "low" A (narrowly missing "high" A because I f'd up on two high-weight assignments).

The class discussions/exercises are simple and can be completed within 15~30mins. Write-up/critique prompts are straightforward but you will want to be detailed to ensure you're meeting requirements. The projects are simple but TEDIOUS and ANNOYING. I won't reiterate what others have said but I will confirm at least that part of all the complaints is certainly true.

Mid-term exam is OK. I only partially watched some lectures and managed to get by with a low B. I'll perhaps try a bit more for the final.

My only complaint is about how opaque the grading is for the project assignments (which, altogether, account for 40% of your grade). While the assignments are absolutely on the easy side, they are also tedious and long-winded; you will definitely lose out on points if you mess around or skip reading the assignment FAQs; no rubrics are provided, you simply have to follow the instructions and hope for the best.

Good luck!

This course is probably easy but it will give you the most frustration of all OMSCS courses. None of the assignments are properly written and you are expected to put in 10+ hours for most of the assignments even though it is an "easy" course. Don't take this course if you don't have to. I hated every single moment of this course and learned nothing. Definitely don't expect this course to be a breeze. This course should have even lower rating.

I really am enjoying the course. I cannot understand why some students gave this course a below average rating. The professor assigns plenty of interesting information to research and practical assignments to complete. This is NOT a programming course. Also you get out of the course what you put in. This is a social ethics in ML course so there will be readings and research papers. In the end, just like the other courses in the OMSCS program, if the course is not for you then don't register for it. In my opinion its a fun and interesting course.

Why are you getting a masters degree if you want to spend your entire time doing busy work?

This was my last course in the OMSCS program. I took it during the Spring 2023 term but had to withdraw due to medical reasons. I decided to try again and take it over the Summer 2023 term. The general topic of the course seemed to be an interesting one, so in piquing my interest, I decided to take it. Even when I couldn't finish it in the spring term, I still was interested (and had a little bit of a head start) enough to take it over the summer.

Coursework

• Class Discussion/Exercises (15%, 12 at 5 points each): These consisted of Case Studies and Exercises. Case studies were categorized as a discussion post to which you had to answer a couple of questions and reply to at least 1 other student's reply. The Exercises were essentially the same thing, but it didn't require a response to other students. There were 6 case studies and 6 exercises.
• Projects (40%, 5 at 100 points each): These consisted of a lot of data analysis, interpretation, and plotting. If you know Python pandas, this will help you a lot, but it can be done with Excel as well. Biggest deliverable is a PDF report in JDF format. Project 1 analyzes advertiser information from a selected social media platform. Project 2 has you pick a dataset and perform a lot of statistical analysis. Project 3 (AI/ML Part 1) has you explore relationships, correlation, means, and standard deviations of a given dataset. Project 4 (AI/ML Part 2) has you use Python to analyze a similarity score between words as well as suggested answers to an analogy. Project 5 has you "look at the impact of computing and applying fairness metrics to "fix" data that could be used to train algorithms associated with learning from credit-based data sets."
• Mid-Term Exam (10%, 78 possible points): This exam covered modules 1-8. It was open notes and open books. You could also use the lecture slides and transcripts. The notes could be physical or accessed on your computer. It is proctored through Honorlock. You have 120 minutes to take the exam consisting of about 27 questions.
• Final Exam (10%, 100 possible points): Calling this an exam is far from the what most perceive as an exam. Very little comes from the modules after module 8. This involved finding an "artifact" released within the last 6 months and doing a report on it, answering questions in 4 various tasks. The artifact also had to have some form of data evidence (research publication, dataset, survey results, etc.) which could be used in answering and justifying your responses. Most of the time was spent on finding the article to meet the criteria. The article I found was somewhat interesting, but I did not learn much from it overall.
• Final Project (15%, 100 possible points): This could be worked on by yourself or as part of a team of up to 4 students. This applies lessons/tasks performed in all the previous projects, assignments, and lectures. This includes graphs, predictions, bias, and fairness metrics. You have to submit a PDF report in JDF format as well as a Jupyter notebook. This involved finding a dataset meeting a set of criteria, analyzing the data, answering various questions, try to mitigate bias/fairness, and analyze the results.
• Written Critiques (10%, 40 points for one, 65 points for one): These consisted of taking a topic, doing some research, apply some thought, and writing about it. The EAV (Ethical Autonomous Vehicle) had you analyze what an autonomous vehicle might do in a given situation and answer an array of questions about it. The What-If Tool had you run a COMPAS demo and answer an array of questions about it. Both had a deliverable in thge form of a report submitted as a PDF in JDF format limited to 3 pages max.

Take-Aways

• Miscellaneous Stuff: I feel there is a lot of busy work, which is a pretty much waste of time (time filler). Extract data, run some calculations on it. Do some interpretation into protected classes. Plot some charts. Analyze those charts. Put all your analysis in the reports. While it could be interesting, most of the work felt like busy work. When it came to the assignments, there is A LOT of vagueness in the specification... to the extent you may spend more time looking through posts or asking questions on Ed than you will spend actually on the assignments. They re-used a dataset from previous semesters, and there is an issue with some data being split across two lines. Instead of fixing it for future terms, you have to ask about it, to which you will be told you can consolidate (fix) the lines. Not taking these 2 minutes to fix the dataset shows their attention and effort to the class.
• Quizzes: Other than the mid-term, there really aren't any "quizzes".
• Lectures: The lectures were pretty decent and able to keep my attention. I feel they could use a little work and some definite updating. You have to watch (mark as done) the Welcome and Course Information modules in order for other lessons to unlock. And, yes, you will have people complain on Ed that they can't access assignments or lectures because they haven't watched these modules. Most of the modules after 8 are not so important. Some of the modules may mention multiple points but only focus on a couple.
• Textbook: The book listed as required for this course is Weapons of Math Destruction by Cathy O'Neil, 2016 edition, ISBN 978-0-553-41883-5. I personally never read or opened the book. Someone was able to find the book for free through the GATech online library, so dig around for it before purchasing it.
• Case Studies & Exercises: These were more of a time waste than anything. Here is a topic. Answer these couple of questions. Reply with a reasonable response to other students, showing some thought.
• Exams: The mid-term exam seemed really fair, especially when you are allowed to use PDFs of the lectures, the transcripts from the lectures, and any notes you may have taken. As for the "final exam," it was sort of a joke, but at the same time, it was a huge pain trying to find an article which met all the criteria set forth in the spec.
• Professor and TAs: The entire teaching staff was great. The TAs have a very obvious present in Ed. They were respectable and responded in a decent time. The professor (Dr. Mandala) was never present, so the TAs basically ran the class.
• Grading: The TAs try to get grades released in about 2 weeks or less, but given the number of students and the condensed summer term, it has taken a little bit longer. Mid-term grades were released around 2 am on the last day a student could withdraw from the course.
• Overall Grade: In the end, my final grade was a 97.83, which got me an A.

TLDR

I started off in the course very strong, but due to an unexpected medical emergency, I ended up getting a little behind on the lectures. As a result, I found myself pulling a few late night weekends working on the homework assignments and case studies. In the end, I withdrew from the Spring 2023 course because my health was more important.

I decided to take this course over the Summer of 2023, which was a good decision. Overall, it is an easy course. It doesn't take too much effort. Just do the assignments and meet the ask in the specs, since they do not provide any rubrics. Do this, and you should be able to pass easily with an A. There is a lot of busy work. The assignment specs are very, very vague (again, more time spent on Ed asking questions or searching for answers than doing the actual assignemtn). The final exam is a pain in the rear and one of my least favorite parts of the course. As others have said, don't expect to gain much knowledge from this class because I didn't, other than what protected classes were. It would definitely be an easy course which could be coupled with another easy course during a Spring or Fall semester.

I used to briefly be an attorney working on civil rights cases. Those who put this class down don't have a real world understanding of the harm that can be done with bad data and algos. It was an easier class. But, if you apply yourself to really understand the intricacies of pre-processing, post-processing, and de-biasing, then it will take a while. Pair that with being not great with Python and you can easily spend 12-14 hours a week on assignments. Frankly, I loved this class for not having to hunt the world for how some weird math concept works as I had to do in other classes.

The students leaving reviews saying this is the worst class they've ever taken are NOT exaggerating. I got flashbacks to high school busy work assignments. You will spend 6-8 hours on a project on mean, median, and modes. The projects are tangentially related to AI Ethics. The assignments are completely straightforward and easy, but you will spend half the time trying to decipher the question. There will be multiple questions asking the same thing in different words, but you will have to create many redundant Ed discussion questions to make sure of this (rather than the instructors editing the prompt). You will spend maybe 5% of each assignment actually thinking about AI Ethics, the rest is formatting your answer.

For one of the assignments, the prompt asks you to display information on 50 different words, each of which has multiple words associated with it. I did exactly this in table format, and of course table was huge, because there is literally no way to display this much information otherwise. They took off 2 points because the table was "barely readable". Lol. Not like I really care, because I got a 98 on the assignment anyways, but really? Why even take points off at that point? I don't even think the graders read the submissions fully.

The single most frustrating class I've ever taken across any of my academic experience. I was looking for an easy A and it was really not even worth it. You will spend just as much time as other classes, and get nothing from it, because your time was spent formatting results in a huge table. Then they will take points off because you did as they asked but they don't like how it looks.

It makes me feel a bit bad to leave such a negative review, because everyone working the course is very nice, but this class is truly appalling and is what prompted me to finally leave a review here. I really hope the program can redesign this course, because it should be extremely relevant and useful in my opinion.

Ive never had a worse class in my entire academic career. You can get As on the exams without watching a single lecture. The assignments are basically just checked for completion, because I know my math was terribly wrong but i never got corrected.

I paired this course with KBAI and it was still extremely easy. I skipped an entire project (10% of grade), did half of the final exam (10% of grade), did a quarter of the final project (15% of grade) and still ended up with a B.

My advice: pair this class with another harder class and just coast to get a B. If you're intent on getting an A, it'll be easy but you're going to get frustrated by the quality of the assignments.

Alternatively, get a B by doing the simple projects and critiques and just completely skip the final project and exams because they suck.

It's frustrating because the intersection of AI and ethics is really interesting and you see those bright spots sometimes but then then those sparks are snuffed out by a course that seemingly the instructor doesn't even care about.

Pros:

• Exposes you to Jupyter, Pandas, and NumPy, which I have never used before. Even so, I was easily able to complete the assignments.

• Exams are really simple. Midterm is a Canvas-based exam where as the final is more a "practical" take-home exam.

• Grading is pretty lenient and TAs are nice

Cons:

• Assignments are uninspired, boring, and descriptions are awfully vague.

• Disorganized (assignments in canvas aren't listed in the way they're due

The class might be easy but super annoying. You’ll spent more time on figuring out what the assignments are asking and doing the god damn JDF format for all the reports and assignments. Ed Discussions is full of questions asking for clarification on every assignment. The answers are also all over the place and not very helpful most of the time. Some assignments the staff hasn't bothered to correct it on the actual assignment but just make an announcement saying the wording is wrong etc. The TAs grading also seems very unfair and kind of sloppy. A huge waste of time and money. Only thing that was remotely interesting was the discussion post topics but even for those the grading was just too serious.

If you want to learn how to properly format in JDF format, take this class, you won’t learn anything else.

I clubbed this course with Network Science. This was a very easy course, but will require patience, and most of the work would be repetitive unnecessarily long. I think the course delivered in what it was intended to accomplish. Now I understand what ethical data handling means. But unfortunately only in an undergrad level.

This is my second MS, and this is the least course load I ever had. If you are looking a easy course that will teach you some basics of ethical data handling while having a life :) (and getting an A), this is the one to do. If you only striving for knowledge, this might not be the one.

AI Fairness is new, but very useful topic to learn. The topic itself probably more relevant than most other courses in GATech. However, I think this course isn't well developed, possibly due to it's a new course and needs some iterations to improve.

The lectures are pretty shallow, most stuff are common senses and you only need to scan through the slide for Mid-term (It's open booked). Final exam is like a project, you only need to echo back some contents mentioned in the course, which is pretty much anything related to AI Fairness.

The description of project is very bad, incorrect information and leads to confusions everywhere. Even TA can't make it clear about the project. But the good thing is the rubrics is pretty loose. They intended to make everything open ended in this course, so as long as you complete the high level requirements (even though it doesn't make sense), you can still get a pretty high score.

https://www.reddit.com/r/OMSCS/comments/11frnuo/cs_6603_ai_ethics_society_the_worst_course_ever/

Please read the above reddit post. Loosely, it summarizes my thoughts. The grading is the MOST lenient of any course I have taken in this program and I am 8 courses in. Sometimes I will leave portions of assignments half-finished on purpose if I do not believe I need that portion for my grade. I would often still get marks for it.

The lectures are fine and I enjoyed them quite a bit. The assignments on the other hand are pure tedium - I’ve had less than 200 lines of code be used for a 15+ page reports due to the copy and pasting of the code or plugging in of different values. The assignment instructions are vague, have grammatical errors, and required constant TA adjustment during the semester. The Taste try to be as helpful as possible and are great otherwise but I believe the amount of busywork could be cut down in this course with a higher emphasis on quality, not quantity.

You don't have to put much into this course to get an A, but you won't get much out of it, either. I found the lectures to be pretty dry and would often fixate on topics in AI Ethics that I didn't find particularly actionable or applicable beyond the classroom. I had taken a similar AI Ethics course at my undergraduate institution that covered a lot of the big themes of the subject (algorithmic bias, major case studies, the role of the government/tech industry, etc.) and I felt like this course regurgitated a lot of that with little unique insight.

The short writing assignments were very simple and I don't recall them being graded stringently at all. Every "programming" assignment that relates to manipulating datasets or using ML models to test for bias can be completed using Google Sheets/Excel formulas, except for the final project, which is slightly more involved. I completed the final project in two sittings of about four hours each with little background knowledge in Python and received a high 90s grade.

This course would be useful to pair with a more difficult OMSCS offering, or as one of your first courses in the program. As previously mentioned, you will not learn much but it's an easy way to knock out 10% of your degree.

Because of this course, I have developed a healthy distrust of AI and its evangelists due to inescapable biases in spite of mitigation strategies.

Do the final project by yourself. It's not too hard.

Overall, I enjoyed the course even though some of the assignments seemed tedious.

People who complained that the course is vague doesn't even know the true intent of the course. If you don't like it, don't take it - there are loads of people in the waitlist wanting to join. Just give it to them.

Indeed, it is a slog. But after taking other ML spec classes, and you need this to finish your ML spec, might as well take it for the easy A right?

You won't get docked points if your analysis is futile, as long as you turn something in. So don't worry if what you're doing is off by some decimal points. That said, it doesn't apply to the exams however.

That said exams are easy if you don't overthink. At the end of the day, this is just a class made from a purely leftist professor who already jumped boat out of GaTech. Just give them what they want and you get the A - guaranteed.

There is a group project, but I figured out with my project mates early on that doing it as an individual makes more sense, and ironically much easier because there is no time sink on waiting for collaborations and what-nots.

Ended up with 99.5% hardly breaking a sweat.

The assignment wording is extremely poorly written, and you have to ask TA to clarify nearly every single one. It is funny that TA also had inconsistent responses. The course is an easy A, and you learned a bit about AI ethical metrics and remediation methods.

This class is extremely easy and I finished with a high A.I believe you won't learn anything useful. I was able to pair this class with another one and had no issues at all. I will say that the class is very interesting and the lectures are engaging. I am very surprised that this is a graduate class, this is more of an undergraduate class that you would take to boost your GPA. The stressful parts are that every week something is due but can be completed in 2-3 hours or 1 day. The Assignments are the WORST. So poorly worded and vague. You will spend more time digging through Ed discussions and understanding the assignment than actually doing the assignment so be vary of this. I can see why so many people complain about this course but I really didn't care because its an easy A.

AI Ethics is a fascinating field. There are a lot of great books and research on this topic. A great, less technical one I'd reccomend is The Alignment Problem by Brian Christian.

The Word2Vec assignment was decent. The homework was trivial but provided some decent practice with data manipulation using Jupiter Notebooks and Python.

Overall, if you just want an Easy A, this is the course for you. If you want to learn something about ethics and computer science, I would recommend reading books and learning about this topic outside of GT's AIES course.

This class is actually decent as long as you have the appropriate expectations going into it. Not nearly as easy as a lot of the reviews would lead you to believe either-- I spent a surprising amount of time using Python for some of the assignments. Some weeks 30 minutes, other weeks maybe 8 hours on doing an assignment very thoroughly and genuinely trying. The course itself is extremely repetitive and you probably won't walk away having learned an awful lot, but I found most of the data analysis we did to be very intriguing and interesting. I also have a newfound appreciation of bias/discrimination in datasets and in AI, which I think is all I could ask of the course based on previous reviews and based on my low expectations of an ethics course to start with.

Pair with another course, know what you're getting into, and give the assignments an honest go even though you'd probably get a 100 no matter what. You just might get something out of it, or at the very least, find an intriguing dataset for the final project that you can pull some interesting conclusions from.

Don't be one of the hundreds of people below saying "omg so easy such a bad class"-- those should serve as a massive red warning sign of what you're getting into here, because it's not for everyone. But for those of us that wanted an easier A, didn't mind the repetition/ambiguity on some assignments, and did have some interest in ethics in AI, it's not bad.

This class is an excellent candidate to pair with another class. I took this alongside Machine Learning. It had some interesting content, but overall there wasn't much. I think you would probably be disappointed if this was the only class you spent a semester on, but this could also be good if you're looking for a light semester as a sort of break.

The assignments are not difficult but can be time-consuming with the tables you have to create. Some of the instructions are also not very clear, and require you to dig through Ed to get some clarity. These can feel tedious at times. The midterm exam is not too difficult, and the "final exam" is basically just another assignment using concepts you learned in the course.

In terms of time commitment, I spent very little time on lectures and finished them with over a month left in the class. Some projects could take up to 5-10 hours, but overall they're not too much time and are decently spread out. The rest of the assignments took much less.

Spent more time trying to understand what to do on the assignments than actually doing the assignments.

Heavily left-wing 'woke' concepts.

The final project literally has you writing a review to support "Professor Woke." From previous semesters' final project:

"Professor Woke decided to create a new course around the theme of Ethical AI. As she browsed the internet for stories about the abuse and misuse of AI, she became saddened by all of the problems out there that developers didn’t even seem to be aware of. She worried that the tech backlash against technologists because of this misuse would override the potential benefits that AI could have for society. She also worried that developers, even if they hear about this abuse, would not be mindful enough to want to fix the problem. She wonders if she should just give up and take those offered jobs in corporate management, which actually pays much better with less worries. It is your task as a student to convince Professor Woke that, as a technologist, you understand some of these problems with AI misuse and have ideas about how to address some of the corresponding bias and unfairness issues."

Save your money, time, and sanity- avoid this class like the plague.

The best way to describe this course is "a easy bad class". Understanding the assignments was a nightmare as they were poorly written and confusing, which took away from potentially interesting discussions that could have been had in Ed Lessons. Instead, Ed Lessons became a spam filled cry for help from students not understanding instructions. This is where all the difficulty came from. I ended up getting low grades on the assignments because of how unclear the directions were. As others have noted, I did not learn anything in this course and I am happy it is over.

I learned something about ethics and being mindful of how to make software. I learned pandas/formatting tables, and python. it was a gentle intro to AI/ML at least, it would initiate you to learn something about classification algos in ML. HOWEVER, the assignments were unnecessarily a drag. If it was written clearly, it would not take me 25 hours to 30 hours just to write tables because the questions were not properly explained, no example expectations, just plain, vague questions. I hope they would re write the assignments. The course has potentials and it is also a good course for anyone who wants to be in AI/ML career

I highly recommend this course to anyone wanting a long term career in AI. The homework and content is easy to understand and finish. Many people take this class without more and deeper thinking afterwards, especially fail to apply this in everyday life and career. By deeper thinking and flexible application, one would figure out pretty quickly the prospect of whatever AI news on the spotlight at the moment, ChatGPT as one. I found that sometimes I am quite caught up with the excitement with the advancement of the new techs, wishfully thinking everyone else is just as excited and more than willing to pay for it as I do. The reality is the majorities are just not like that. They only pay if the new tech satisfies this and that, basically what is being taught in this course. And my job and salary rely heavily on the majority people's payment to continue. If they say the new tech is cool, but they will not pay because of ethics, equality, etc issue, the new tech is only an exciting spark in my life. To plan long term career goal, I found it is wise to invest in this course and it helps to choose the one people, the majority people not just me, find exciting and are willing to pay.

ChatGPT could pass this class.

(Which is good because I was taking it alongside AI)

Requires an active social media account. They suggest Facebook. If you don't have one, then the suggestion is to sign up for social media and use it until you have at least 50 data points of targeted advertisements. Ironic that an ethics class would require students to sign up for a toxic social media account specifically to divulge personal information.

Very easy! Check out a full review of the course here https://youtu.be/wQOSeCC5oss

Ugh I said "busy" in my review bellow, I meant "easy"

Bottom line: Class is ridiculously busy and you learn basically nothing (unless you are in high school or may be first year undergrad).

I learned some formatting techniques for python (tables, plotting etc. ). I also learned what the protected domains and classes were (literally just the list of what they are).

Three of the project homework assignments were basically the same. Midterm was really simple (just general understanding of the ppt charts). Class discussions and written critiques were just follow the rubric.

Everything was just follow the rubric. I do not even know if they really looked at the answers and how you got them. I thought I bombed the final project and the final. I was over the class and was OK if I got a B (which with getting close to a zero on each I would have had). I got 100% on both . Not a brag but again just to show if you follow the rubric you score high. I ended up with a 99.04% total for the whole class, the highest I ever had in any course.

So getting an A is simple. The problem is you really learn nothing as I said above. The measures of bias and fairness were never explored. Which is a bit of a disappointment. You come to think that AI Ethics is nothing more than removing bias terms from a ML dataset (or just weighting against a privileged group). The subject seems very irrelevant and that people out there make a bigger deal of its importance than necessary compared to the solution for the problem. Maybe this is wrong, but you don't really know after taking this class.

The other reviews aren’t exaggerations. This class really is a joke.

The projects are nothing more than pointless busy work (there’s not even a paper format you need to follow) that do nothing to teach you about the subject. Half the assignments are literally “compare these simple statistics you learned in elementary school and then use this open source library on the data”.

If the projects weren’t enough of a joke, the exercises and critiques are even worse. These are what all of them were: Read this short paper and write a paragraph summary then comment on two of your classmate’s summaries or do this 5 minute activity and write a 2 page paper on it.

The final exam was to find an academic paper and summarize it in 2 pages. Also a joke.

Perhaps the most egregious thing about this course is that for some reason it would take over a month to grade any of this.

If you’re looking for an “easy” class that is also worthwhile, take Dr. Joiner’s HCI course. As a side effect you’ll even learn more about ethics than you do in this class plus what you learn is applicable to industry unlike this joke.

This semester, I took my 7th and 8th courses. So as of now, I am 4/5 done with this program, and this course is the only one where I feel like I have 100% wasted my money and time by taking this course.

It's just bad. You won't learn anything interesting about morals or ethics. You won't learn anything interesting about artificial intelligence. And you'll only just barely learn anything interesting about big data and how it's manipulated.

If the lessons are bad, the work is abysmal. It's all easy, but some of it contains ludicrous amounts of busywork. Half of the assignments are made up of multiple steps, which are comprised of things like, "Run this algorithm on this list of 50 sets of words in a text file, and then make a table in a Word Document showing the different outputs for each set of words." or "Create 3 graphs each for these 5 datasets and then describe the differences between each one". You are not going to get better at programming, but you'll probably get better at copy and pasting things into a table, or screenshotting MatPlotLib outputs.

This class is just terrible. But it is easy and doesn't take up that much time. So if that's why you're taking it, go ahead. If not, stay far away.

This is the worst class I have ever been in. What an absolute waste of time. Busy work, unclear directions, and teaches nothing. Yes, they grade very easily and you will get an A. But the frustration is not worth it.

I really thought the reviews for this class were exaggerating. But no, this class really sucks and is an embarrassment for OMSCS. It takes the fascinating topic of AI Ethics and butchers it. I would often work on the assignments in bulk and take weeks off until I did the same again and repeated. Frequently, I would not have work to do for 3 weeks since I worked ahead. The material is trash, I learned nothing as it was very basic material. The TAs aren't that good either, usually giving generic answers to questions which led to assignments being a jumbled mess of busy work. Overall, this class sucked.

The workload is very light, the class is an easy A so if you want that, go for it. But be warned that it'll probably just make you hate the topic and question OMSCS' decision to keep this class.

Worst ever

This course was filled with an insane amount of busywork. Also... I went in not knowing much about Ethics, and I came out knowing even less. The best comparison for this class would be sitting in a parking lot, and being given the assignment "count how many red cars, blue cars, and black cars there are".
Literally. Easy A. Not worth it.

I feel the workload is significantly amped up, or maybe I feel so because I took it in a summer semester. You always have a homework or assignment due the week, and the assignments increase in difficulty - with each of them taking a minimum of 4 hours to maybe a maximum of 8-10 hours.

With that out of the picture, I still enjoyed the course though it is not technically/conceptually difficult. There were many thought provoking aspects to it, and the course helped me to now look at tech through a different lens that I didn't previously possess

Many students are giving unfavorable reviews, but I think this course is great, because it addresses an aspect that is usually overlooked by tech geeks, which is how to design ethical systems for the human society.

I admit that sometimes this course is technically easy, but ethics is not about solving a difficult mathematical problem, but about the impact it gives to the lives of thousands of people. Throughout this course, I could see students giving simple answers without elaboration, and reaching conclusions without justifications. I think that is why students think the course is easy.

Being a student, if you insist enough to provide justifications on every judgement, then your answer on each ethical decision should be in much greater details then the other casual students. I think that is the attitude that we should possess, when we make design decisions that affects different races or genders or countries of origin. (I have worked in government, and bureaucracy requires detailed justifications on every step along the way to reaching a system design for the general public.)

Incidents on Facebook (target advertising that excludes a certain race), COVID vaccine passports, ArriveCAN that uses data analytics or even prediction models to trace the tested-positive or unaccented , China face recognition system and COVID health codes, nation-wide surveillance... These are systems that greatly affect or even control our lives. These systems would become better (or cease to exist) if every system designer or developer knows the AI ethics good enough, and are bold enough to do the "ethical" thing. In additions, political issues such as LGBTQ+ and race induced debiasing (which is usually supported by "left wing" or Democrats), while over-debiasing may become an issue if a heterosexual white man is being discriminated instead (which is usually a concern by "right wing" or Republicans). I think a balanced discussion with supportive evidence would be great answers to many homework and projects in this course.

Overall, I think this course introduces a lot of topics that we can further drill on, incl. protected classes (race, sex, country of origin, etc.), laws, cheating with statistics, aif360, Google what-if, and other contemporary issues. Even if this course is (mathematically) easy, I would still encourage students to take it.

If you're planning to take this class you already know what you're getting yourself into. Just like everyone else has said this class is easier than a high school class. The assignments are pointless and repetitive (Like copy/paste lots of numbers pointless). I did not watch a single minute of the lectures and got a 99.7% on the midterm and 100 on the final. I did each homework assignment the day it was due and had little problem. I'd recommend this class if you need to get an A. Just turn everything in and I promise you will get an A.

Fairly easy class overall. As other students have mentioned earlier, the assignments are very repetitive and time consuming at times. This is a great class to take if you have a lot going on in your life.

I very, very strongly disliked this class. I have bachelor's degrees in philosophy and applied ethics. This class should not have ethics in its name. I was hoping for an AI ethics course. This is a data science course.

This class is extremely easy, so its great if you want to pair with a harder class or if have demanding work/home life already.

With that said, this has been my least liked class so far (out of 8 total). The entire class content could have been condensed into a 3 or 4 page paper. To be clear, the content is important - people making and using ML/AI need to account for potential bias in the training data - but this just shouldn't be a semester long class, or a masters level class. The assignments are extremely repetitive/boring. They are easy, but you'll come to dread doing them because or how pointless they are. You will spend more time reading the instructions than you will doing the assignments.

Like many reviews say, this course is easy if you have a data science background or you have a teammate with such experience. Working on it alone with no prior experience definitely poses some challenge. A lot of time gets spent on plotting graphs and figuring out how to do the requirements. A realistic week might be 10 hours/week while doing projects and having no prior experience.

I feel like this course got tweaked to increase the workload and the end result is you have a lot of pointless busywork in assignments and some assignments can be kind of challenging if you don't have a data science background because it's asking data science questions without teaching you how to answer them

The ethical discussion topics were interesting and can be banged out in like 20 minutes

Assignments are kind of hit or miss with some being a decent amount of work

Take this course if you have a data science background and are looking for a super easy course or if you don't have a data science background and are looking for a semi-easy course

Rating this course as "disliked" because there is a lot of pointless busy work (e.g.: Make a graph for this 1 set of data and then repeat the process 10 more times to create a total of 11 graphs so you end up wasting hours of time without learning anything)

I regretted not pairing this course with a more challenging one. This course is very easy, although some assignments can be annoying because you are creating the same figure 20+ times. The lecture is interesting but does not go into too much depth in bias mitigation.

TAs are great and the grading is fair. My main suggestions are 1) pair it with a more difficult class, or save it for the semester that you barely have any time for class; I spent on average 2-3 hours a week; 2) the group project allows for individual submissions. Do it yourself instead of finding a group. The project is very straightforward and I was able to complete it in one day by myself and got 100/100. You will probably end up wasting more time communicating with group members.

This was my 4th class in OMSCS. I was in the middle of a job change, moving from one state to another and took this along with INTA-6450 ( Data analytics and Security). This was an easy pair except for the week i was traveling. I was able to work ahead in AIES and finish the assignments to get some time for my move.

The assignments were easy to finish but gave a lot of learning to me since i am new to pandas and Scikit-learn. It seemed like a lot of unnecessary work plotting 20 graphs with different combinations and writing reports. This class is a good introduction to the data analytics and provides exposure to jupyter, pandas, matplotlib, and makes you familiar with handling/manipulating data and displaying graphs. For some of the assignments, instructions were not very clear, so there were lots of piazza threads asking for clarity. Vijay from the TA team was very active and helpful in responding to all the queries.

The mid term exam was a mess for me because of honorlock issue that kept blocking me from opening any document on my laptop even though the exam was open book. I did score around the median and was able to get 90+ in overall.

The final exam was just about picking up an article related to AI ethics and write a report about it with the supporting data. The difficult part was finding a recent article/video published within last 6 months that had enough supporting data reference in it. Once you find the suitable article, it was pretty easy to write the paper.

Final Project was a group project (group of 4) where i struggled a bit because most of the group members were absent due to personal and professional reasons. Thanksgiving was also one of the reasons our group couldn't meet. Most of the team members were more comfortable working with Excel and didn't want to use python for the project unless it became absolutely necessary. I had to do most of the coding 1 day prior to submission for the steps where we needed to submit the code. Most frustrating part was someone in the group asking : why we used certain function/method at the last moment when we didn't had time. So if you get a good group, the final project should be easy-peasy. The grading on final exam and project was very lenient.

Last week of the semester was little busy with final project, final exam , critique and discussion all pending around the same time.

Overall, an easy class with some learning for me.

This was my 8th class in the program, and I have experienced both higher workload classes (AI, recent CP to name a couple) and this class is around half the workload of those.

I went into this class a little skeptical, but I was pleased with the course. The lectures were well done. I can't say that all the lecture material was needed for the projects and exams, but certainly make sure you pay close attention to protected classes and domains as those show up on nearly every assignment.

If you're looking for an intro to the data analytics space, this is a good prep for that with exposure to jupyter, pandas, matplotlib, and gets you use to handling data and displaying some graphs.

Grading was fair in my opinion as long as you "checked the boxes" of everything the assignments asked for. Most assignments had some vagueness, but found that a lot of it can be done at your own interpretation as long as you describe what you did.

The course presents very interesting topics and discussions. It also serves as a great introductory course for data science, pandas, numpy, etc.

As others mentioned, the course itself is easy when compared to other courses in the OMSCS program. However, easy is subjective. The course is easy for those already experienced in Python / Pandas, etc. but it may be challenging to those who lack experience with Jupyter Notebooks.

While I did have experience in Python, this was my first time deep diving into Python for Data Science. By starting assignments early and watching tutorials on the side, I found the homework to be very easy. However, it is time-consuming. My advice is to start early and don't leave your homework for the last-minute.

My only complaint about the entire course is the way the instructors wrote the assignment instructions. I found that I often spent a lot more time trying to decipher what the instructions were asking for than actually working on the assignment itself. I found that classmates experienced the same issue and would post for clarification on Piazza. If a clarification in Piazza was made, the instructions were never updated to reflect that clarification. Because of this, I had to skim through many posts to ensure I'm completing the assignment correctly.

Anyways, the class is also composed of discussions which are very easy. It's just a matter of answering the prompt and responding to two other classmates. The midterm was open notes and consisted of a mix of multiple choice and write-in questions. I felt that there were trick questions but received a passing grade either way. The final exam was converted into a take-home essay that had its challenges but it wasn't difficult. I felt that the grading was more than fair. I received perfect scores in all assignments minus the midterm. I am in no way an expert in Pandas but this course is definitely do-able as a beginner.

I can see how this course would be a freebie to those whom are more experienced in the field of machine learning. I can see how someone who has taken courses such as ML or AI would find this class boring. However, the course is meant to be an introduction. I enjoyed it.

The class overall is very easy compared to other OMSCS classes. The content is interesting and somewhat engaging but it defintely won't be nearly as technical as other classes. As a word of caution, though the class is easy some homeworks and projects are time consuming. The trick for these is to simply just put down coherent thoughts and as long as it makes sense you'll be able to get High A's. The midterm was suprisingly harder than i expected but nothing crazy. Final project is a good chunk of work however so try to find a group for that.

Yes it's a "freebie" but it also does its goal of teaching you things without beating you over the head with it and boring you out. While the material and level of effort is more indicative of an introductory class, the discussions and lecture content are at least pertinent to current events and can be applied directly to them. I've taken other "freebie" classes that were just absolutely painful to get through (boring projects, boring reading, boring lectures, etc).

As other reviews mention, this mainly focuses on discerning bias and ethical harms towards protected classes (age, gender, etc). There's some very rudimentary statistics and bias mitigation techniques taught through the lens of discussions and homework assignments where you apply them. If you want you can pretty much skip the lectures altogether and just do the assignments without too much difficulty.

Exams are very simple and straightforward, no gotchas, simply just reviewing concepts that will be (at that point) well-reviewed and discussed a lot. I can see (from ML point of view) how this class may be frustrating (it doesn't really do nearly enough exercises in bias mitigation/examining ML implementations and strategies for ethical concerns) because it mainly focuses on the social side of this topic, but unless you signed up for the class without reading reviews then some part of you just wanted an easy class with low level of effort and this is exactly that.

Get a grip, y'all. Take a hard look at yourselves.

• You came to OMSCentral to read the horrible reviews.
• You are under no obligation to take this class. There are other ML electives available. RL, DL, etc.
• You basically chose this class anyway (1) as a freebie to fulfil your ML elective; (2) because the workload is low; and (3) you don't need to spend on your non-CS/CSE token.
• You came back to OMSCentral to remark further that this class isn't worth your money. It's a freebie, duh.

See this vicious cycle?

My background is in data science. I took AIES because I am in another course this semester that requires over 20 hours/week, so I just needed something light to pair with it. For this purpose, AIES was satisfactory for me. The TA's (especially Vijay on Piazza) are very responsive, timely with grading, and generally helpful. I think a lot of student questions were due to a bit of ambiguity in the assignment descriptions, so maybe they could look into improving those.

I gave this course a "Dislike" because the only thing we've covered all semester is "Protected Classes". Protected Classes are features in a dataset like race, age, sex, etc. You don't want to build an AI that incorporates historical biases due to an unbalanced distribution of such features. In the preceding sentences, I have covered what you'll spend a whole semester on. It's tedious to do assignment after assignment on the same thing. I do think it's possible to expand the breadth of topics covered in the class while keeping it brief and engaging, but it would need to be overhauled.

The lectures are pretty good. Prof. Howard picked rather relevant examples of AI ethics issues. I thought they were well structured and clear.

There are only about 5 coding assignments. I think they took me about 5-6 hours each (but I have 3 hours/week since other weeks were just discussions on the class forum, which were ~30mins to do). The assignments are more tedious than difficult (little things like formatting tables, copying and pasting text, etc. take up the most time). They kinda felt like busy work. If you're learning Python and Pandas/Numpy though, I could see them being a good warm-up to harder Python-based courses in OMSCS.

I haven't taken the final yet. The midterm was pretty easy, and I personally don't think any preparation at all is needed to do well on it.

The final project is perhaps the most useful part of the class if you do it all on your own instead of in a group. I learned a decent amount with it regarding fairness metrics to gauge "unbalancedness" and how to reweight the data to improve fairness.

This was a waste of a class. I, like many others here, took the class as a freebie, but I can say that I sincerely learned nothing. It was extremely repetitive- I think the professor's emphasis on protected classes was important, but every assignment and video focused on some iteration of that. We didn't delve into different schools of ethics, and no part of the class required any critical thinking. My undergrad ethics class was far more challenging and thought-provoking than this one. It's disappointing, because such an important topic deserves to be treated with respect. I agree with other reviewers who mentioned that Joyner should remake this class. It'd be far more engaging, challenging and thought provoking.

Other reviews already provide a synopsis of what to expect. I took it as I wanted a light course load with straightforward assignments. The only thing that is somewhat time consuming in this course is the homework assignments, but they can be knocked out in a half day or day if you're somewhat familiar with Python using some of the data science libraries. Given the other reviews, I went in with low expectations and was actually a little surprised that some of the Python homeworks weren't as straightforward as suggested. Not difficult but not as gimme grades as what I anticipated.

Other than that, everything else is coming up with reasonable answers for participation. The material is a little repetitive about what bias is and most of the information is common sense. The midterm was a little nitpicky but nothing too bad to ruin your grade. As others mentioned, take this course if you want a straightforward course or a lighter workload. Happy I took the course after reading the reviews as they painted an accurate picture about what to expect and motivations to take the course.

I agree with most of what is written already that this class had a lot of potential since the topics are of incredible importance and relevance in today's society, but the execution is poor.

I feel like if Joyner took over this course, it could be an HCI-style class with weekly writing assignments to make you think deeply about how ethics play a huge part in everyday decisions and is something that computer scientists need to grapple with as ethics start to take center stage in many of today's innovations and technologies.

I would not underestimate the assignments. They are tedious and took me quite some time to complete. They are not hard by any means but they require making a ton of graphs, data cleaning, and interpreting the instructions. The final project I chose to do alone and oh boy, it was rough (time wise). I got it completed but it was no fun.

This is a good class to pair or if you just want an extremely easy semester to rest up from harder courses. The ML track has a ton of time consuming courses and it's not a bad thing to have a freebie to help you to gain your strength back.

This course is disappointing because it has the potential to be a really interesting topic and it completely falls flat. The lectures cover some interesting material, and some of the supplementary readings are insightful, but the graded work is so easy that a dedicated middle schooler could probably get an A in this course. The most complicated assignment in this course requires you to plot about fifteen graphs and calculate mean, median, and mode of some data.

There's "exercise" (the class's term for a discussion prompt) that requires you to use an online tool to attempt to mitigate bias in a dataset. The tool links to a python library that you can use on your own to attempt your own manipulations to datasets. In the project that focuses on dataset bias, the course staff recommends against using this library, as it is somewhat complicated to use.

This is a computer science masters program. Having a course that requires effectively zero computer science knowledge or ability devalues the entire program.

I just took the midterm for this class and it was the worst midterm I've ever taken, in my life. It was very easy: What is a mean, median, mode, etc. but that's not my complaint, I was never expecting to learn a lot in this class based on the reviews here. One of the questions asks you to calculate the Mean of ~25 numbers that range from 10k-100k, from a PNG of the numbers, so you have to copy them by hand into the Honorlock calculator and just hope you don't mistype any of them. What is the purpose of a question like that???

The assignments so far are ok. They could be worded more clearly, but they're easy. This class could be vastly improved while still being a freebie. This class actually has a decent amount of potential, the topic is interesting and Python is a useful skill. It's just executed poorly. Take it for an easy but tedious grade.

End of Semester Update: I think the final was way better than the mid-term. But just know what you're getting into here. An easy A, but you won't learn anything, and it's somewhat tedious.

The TAs are pretty responsive, which is nice. If you want an easy A, take this class. My review is negative only because I'm disappointed. This subject has a lot of potential but this class really doesn't get deep enough into anything for you to learn anything.

I found the subject matter important and interesting but unfortunately it just doesn't dive in very deeply. In my opinion a graduate level course should not be wasting time explaining/testing on Stats 101 concepts (mean, median, mode...)

Aside from the lack of depth, my one big complaint was that the class was paced such that there was a ton of work due the last week of class - if you worked ahead it might not have been too bad, but if you stuck with the class schedule like I did you ended up with a week that was significantly busier than the rest of the class.

Basically, if you want an easy semester or need to quickly take care of a Machine Learning or Interactive Intelligence elective, this class is a good fit for you. If you want to learn a lot look somewhere else.

I read the reviews prior to taking the class and took this one because I needed an easy summer course. I got what I wanted, most weeks I put in 2-3 hours of effort. Not so during the final week, where 1/3 of the grade was due all at once. That week I put in over 30 hours, which was my fault because I decided to do the final project alone. In the end, was it a good class or a bad class? Not as bad as I thought, but not very useful either. My biggest lesson was that people who complain about bias are often unhappy about unequal outcomes. Data can be bad, and bias does exist, but if you take a test and the other guy scores better, you're not doing something right, bruh. How do you make this class better? I'd teach it as an intro to ML using Python, using ethics cases for the homeworks. Right now it's 80% wokeness 20% Python, the right ratio is 20/80. And that's it, I'm sure I pissed you off. Now go eat your French cries.

LOL

This class is pretty useless... Most of the projects are really just busy work and you don't really learn much of anything. Most annoying thing other people have mentioned are the graphs and tables you have to generate to their specifications. Things like make a table of different means and medians.

Took this class for an easy A but straight up didn't do some assignments if they looked too annoying, but an A was definitely very attainable.

Class can be summarized as:

• Sometimes there is bias in datasets since people are biased.
• Getting rid of bias is hard.
• Bias is bad.
• Make a graph on why bias is bad.

I have taken back to back, very demanding classes (CP and AI), for the last two semesters so I really wanted a major mental break without delaying my graduation. I knew that this was one of the easiest classes in the program, and that is 100% the reason why I took the class.

I agree with other reviewers that it's extremely easy (probably high school difficulty), but honestly I think most students who take this are counting on it to be a gimme class to either take a break or get a guaranteed A. I never spent more than like 3-4 hours a week on this class, and could complete each of the projects in one sitting. The projects are python coding assignments, and there are a lot of case studies and written critiques that need to be completed as well. Some of these written assignments are just busy work, and you definitely need to be on top of when things are due. There are frequently projects and a few case studies due on the same exact day. Also, I spent more time trying to figure out how to graph things in Python than actually completing the stat/AI part of each assignment. I get graphs are important but I felt this was a big waste of time.

I wouldn't recommend this class to anyone who actually wants to gain a deep understanding on AI ethics, which is a shame. This is actually a topic that I'm very interested in and is becoming increasingly relevant in everyday life. I will say that the instructor for this class changed this semester somewhat at the last minute, so I could see this class change and evolve in future semesters.

While this class is very easy, its also like watching paint dry. It you want an easy A be prepared to make 50 of the same graph, to watch boring videos, and to work with un-inspiring data.

I think its an important topic, but this class is borderline high school level in difficulty.

This is a great intro to python, statistics, and machine learning course, but not great for data science and machine learning practitioners.

Listen. I know why you want to take this course and you know why I took it. And you're here to find out whether it actually is what you think it is. I work in ML with Python/Pandas/NumPy everyday, so my review is from that perspective.

You will learn almost nothing, put in almost no effort, and wind up with an A. So if you're someone like me who wanted a breezy semester between tougher classes or someone who's doing the SDP/SAD/CN/DBS/etcetera ultra easy degree path (you know who you are) and you just want to check off another class on your way, then take this.

So why do I strongly dislike this class even though it was exactly what I expected? Because it doesn't change the fact that the course is stupendously easy and belongs nowhere near any master's degree.

Do enjoy doing busy work similar to your 5th grade homework assignments?

Do you like completing assignments where the greatest challenge is unpacking the terrible instructions to try to understand the task at hand?

Do you like making the same graph 50+ times?

If so, this is the class for you!

This entire course could be reduced to an hour long lecture.

Good class if you're just looking for the path of least resistance to earning this degree, but a complete waste of time.

This class is incredibly frustrating. I read the previous reviews that it was boring and easy, but thought it could still be interesting (you get out what you put in, right?) and I wanted an easy class for summer. But I regret taking this class. Let me start by saying I've been a statistician for 20 years, and I already have one master's degree in stats. This isn't even stats 101, this is who-knows-what-garbage. A lot of the stats in here are just plain wrong. Mode is not a valid type of average. Mean is average, mode is most, and median is middle (that's stats 101!). There are whole lectures on misleading graphs, but then in the homework projects, we are forced to create nonsense graphs that don't mean anything and don't show anything useful. Why??? And why are we calculating correlation on categorical variables that we've have to make up some nonsensical ordering for? It doesn't give any useful information, and there are plenty of statistical test for association between categorical variables. And when the instructions are not micro-managing you into producing garbage, they are just plain incoherent, and everyone is confused all the time. This class has been out long enough now that everything should have been worked out. But don't waste your time. It's not interesting, it's not useful, and much of it's not even correct!

The other reviews are coming from people who might have already completed other hard ML/AI courses, and to be fair they are playing spoilsport for this course. It is like someone who has already completed GA is complaining that SDP is not as hard as GA. Different courses have different goals and difficulty is not necessarily a goal that profesors look out for. And this course is not an easy course if you are new to AI and python in that case I guess this is a very good course, There are 6 time-consuming projects, around 6 writeups and two exams and also debates.

Ok, so this is definitely not a rigorous class like AI/ML/RL/etc. It's more of a general survey of the many ethical considerations around building AI Systems, designed to get you thinking about these issues. You will do lots of data-cleaning and basic statistical analysis on a number of datasets. I really enjoyed the last half of the class where you focus on bias mitigation techniques and also how to measure fairness. The projects can be a bit tedious, but are still fairly easy if you are comfortable with Python. Midterm was open notes and very fair (no trick questions). The final exam was a take-home essay that took ~ 3 hours to complete.

Worst Course in whole OMSCS curriculum. This is my second last course. Apart from not learning anything this course expects you to have a good python/ML experience. Assignments were extremely boring and ambiguous. The TAs looks like nut jobs, writing ambiguous answers to ambiguous questions. I regret taking this course.

This is my last semester and I took this course along with GA. This is the worst of all the 10 classes that I took. I learned very little. Homework assignments are super boring and tedious and teaches nothing. A waste of time. Stay away unless you are desperate of getting an A.

There is benefit if you do not know anything about python/pandas and other related modules and if you re super rusty in statistics. If that is the case this class will be a valuable force to help you level up.

If you already know ML/DL and take this class, it will feel like a shore. There is extensive data cleansing/normalization and manipulation that is required and that will encompass a great deal of work.

The class overall is ok, and to this point I have not received my grade yet. If what everything they have been talking about this class being easy, it might be because of the lenient grading. This leniency in grading seems to be true so far. However, if lenient grading were not the case, I would have to say this class is not as easy as it has been pointed out to be. There is substantial work and certainly some several hours that you need to put in to get the assignments finished. Also, the assignments are at times not explained well which needs one to ask in piazza a lot to get clarification.

Not a graduate level course. A high school student can make it without putting much effort. The quality is extremely poor even for an elective course. I got nothing from the course. If you are interested in the AI ethnics, there are plenty of online resources much more useful than the course material. I wouldn't recommend it for anyone except you don't care about learning and just want an easy A.

Case Studies

Read a linked article and answer a few discussion questions, then respond to two other students. Should take 15-30 minutes each, and overall seems like 5th grade busy work

Homework Projects

The AI/ML assignments were large datasets that involved some mindless drivel of re-organizing data, filtering, and creating graphs/tables. I’ve never used python for this sort of thing, so I just knocked it out with excel. ~8 hrs each

Final Project

Not yet completed, will update after

Midterm exam

Open notes and proctored. Since it’s open notes, no studying is required...most of the answers are simply intuitive

Final exam

Not yet completed, will update after

Overall

Simple course; you may often wonder how this could be considered graduate level. This is an elective, so it checks a box. The content is interesting, but overall seems like a waste.

This is by far one of the most pleasant and well-run courses in the program. It is an excellent start to the program and the course material is interesting. There is a light pace to the course, great for pairing it with another class, or just taking an easier semester. The professor for this course is incredibly accomplished, and the lectures are strongly relevant to the material covered in the midterm. Course material, assignments, and exams all tie in very nicely and this makes for a great student experience.

I took this class and paired it with AI for Robotics, with the expectation that this would be the easy, non-technical class, and I was right!

The class is certainly very easy, and as others have mentioned, I wish that it actually delved deeper into either how to apply AI/ML techniques, or how the bias mitigation techniques work. I think that a lot of the concepts covered could have been condensed. The midterm and final exam project were both very easy, and this class did change my perspective and make me very cognizant of how AI can negatively impact certain groups. The projects were tedious and didn't necessarily equate to a lot of learning, but still, the workload for this class was extremely light (which I wanted).

You don't put too much into this class, but you don't get too much out of the class. If you are looking for an easy class to pair with another class (plus I heard it might be an ML specialization elective now?), you will be satisfied, but you don't learn that much, so I am a bit neutral overall.

I have a more in-depth video review, where I walkthrough the projects and the downsides of the class here: https://youtu.be/pe4EnivoRHk

Took AIES alongside GIOS as my 6th and 7th classes in OMSCS. I had previously taken AI, RL, CV, ML and HDDA.

This was without a doubt the easiest class I've taken by far. I feel that the first half of the material could have been compressed a lot more so as to leave more time to deep dive into the various bias mitigation strategies. As it is, after the class I still have only an extremely surface-level understanding of the different ways to mitigate bias, which is much less than I expected out of a graduate-level class from Georgia Tech. For example, there was an exercise where we discussed bias in word embeddings, but didn't learn about how exactly to reduce bias in these word embeddings.

I would have liked this class much better if there were lectures that explained the inner workings of bias mitigation algorithms and assignments that made us implement some of these algorithms from scratch, or at least tested our understanding of how they work.

I think you're going to get out of this class what you're going to put in. This is a pretty easy course. There's a book for the course to read called Weapons of Math Destruction, which is a fast read and you can front load. You'll interact with it rather minimally in the course, but it's a good primer on what's to come.

You'll probably want to come into this class with some proficiency with Python/Jupyter Notebook. You can get away with just using Excel for the first two assignments, but over time you're going to have to be doing a lot of stuff that will need Sci-Kit, Numpy, and Pandas. The last two assignments also require you to reverse engineer/understand some Python libraries from IBM and Google for helping combat bias in ML algorithms.

In any case, I think you can probably do the bare minimum and spend a very small amount of time in the class and still do well in the course. However, if you pour a lot of time into doing things well in the course, it's rather rewarding. I came into the class with some knowledge of Pandas/Numpy and some Machine Learning algorithms, I emerged out of it feeling much more confident in implementing things with Scikit.

I liked this course a lot. I went with expectation of getting an easy A ( got it as well). This course is sitter A course. The only way not to get an A is to not write the assignment and exam. But grades aside, after taking this class I realized that this course made me think on important aspect of software engineering and ML. In each of the dataset I used for assignment I was able to find the bias. This was an eye opener course for sure. The course does not have any specific reading , but the course material is thoughts/ideas of your peers. The discussion is the real material. 50% of the class is introduction to ML and python. Since, I have background in both so I did not bother to spend a lot of time in those areas and passed the tests and assignment without going through the lecture. So people with similar background might find this extremely easy too. remaining 50% was not difficult at all. Its just a thought process which the professor wants to develop in all of us. No fancy algo, just ways to find evidences of discrimination based on data, learning and reacting to others views. Conservatives across all the regions in the world might dislike the course.

Do not take it if you want to learn things. I did not learn anything useful in the course. The course materials are poor and the assignments are time wasting. The course tries to teach you the bias of AI/ML with no "series" implementation of any algorithms/programming assignments. Most of time you only need to "talk". I think the course is a good introductory one for undergraduate students and cannot be a graduate-level CS course.

This class has a lot of potential, but fails in its execution. It is an important topic, and the lectures are outstanding. However, the material is not graduate-level (readings are websites or sources such as the NY Post); there are few academic/peer-reviewed articles used, and the main textbook (though an entertaining read) is a mass market paperback.

The biggest issue with this class is lack of clarity in the assignments; you will spend the majority of your time trying to decipher what exactly the instructor wants you to do. This could be fixed easily if materials were proof-read prior to being released. The mid-term suffered from the same issues: questions were poorly written and unclear.

Overall, the quality of this course is poor, which is a shame because the subject is great.

[Updated after completing the final project and final exam.]

TL;DR The difficulty of this class is on par with a high school course, and falls short of meaningful discussions into biases of AI/ML.

I really wanted to like this class. It has so much potential with its content being incredibly relevant, modern, and of huge impact. Unfortunately it doesn't deliver and will leave you simply saying "There is a problem with bias in AI (hopefully not new information to you), but I don't know what is being done in industry to address it." The final project very lightly touches on bias mitigation techniques, but you'll mostly just call library functions without understanding how the bias is mitigated. In its current standing this course feels like it should be offered in a MBA program, not a graduate-level CS program due to its shallowness.

Pros

• Minimal time required Of the 8 classes I've completed, this course has by far and away required the least amount of time. I have been able to knock out the lectures and weekly deliverables on Monday night. I've been able to complete each project in a single session on a weekend afternoon.
• Active instructional team The TAs do an excellent job of answering every Piazza question. Professor Howard participates as well, answering questions and occasionally posting articles to generate discussion.
• Easy If you're looking for an easy course, I don't think OMSCS gets easier than this. I have a 100 so far, including a midterm exam. The work required for this course is incredibly easy to complete, as evidenced by the very high assignment averages.

Cons

• Pace The course drags its feet on content that could be covered in a fraction of the time. Time is spent covering middle school-level descriptive statistics like mean, median, mode. You will complete multiple projects that simply process data and report descriptive and inferential statistics. To me this felt like Week 1 - 2 type material, but instead it took us all the way through the midterm. I wish this course spent time understanding the math and theory behind defining fairness and mitigating bias, but it gets brushed over in the last two weeks of the course in the final project.
• Lack of Meaningful Discussion Most weeks we are required to write a response to a case study or writing prompt covering some ethical concern. Part of the assignment usually requires you to respond to 2 classmates. The problem is that there is no incentive to engage in thoughtful conversation. Most responses are along the lines of "that's a really good point" or "i agree with what you said about X." Additionally, there is one discussion board for the entire class, so it gets extremely cluttered and infeasible to keep up with.
• Easy This can be a pro if you're looking to double up courses in a semester, but if you're wanting to be challenged in understanding bias and bias mitigation techniques in AI or to be pushed into evaluating ethical concerns at a deep level, you'll be left unsatisfied. I remember the exact assignment where I stopped trying for this course. We were discussing bias in NLP, and were provided a research paper from Microsoft about addressing the biases. After reading it, studying it, and posting my thoughts in my weekly response, the only responses I received were "yeah ok kewl research but the devs need to watch out for their own biases." face palm Based on my observations of Piazza, this course attracts the weakest OMSCS students who post questions like, "When the instructions say X, I just want to confirm that I should do X?" So it's best to just ignore Piazza unless you have a specific question.
• Projects The projects are mostly just following a step-by-step series of instructions. I just completed the current project covering word vectors while watching a football game. It's "do this" followed by "do that." There isn't much thinking involved. The projects don't push you to understand why things work the way they do.
• Supporting Materials Like I mentioned earlier, sometimes papers will be listed as an optional reference, but you won't actually need to read them for this course. There is a great text listed on the syllabus, Weapons of Math Destruction, but it's never referenced in the course which is a missed opportunity.

Overall good course. Not too time consuming with busy work. Assignments in python. There are written critiques and assignments. Two exams (midterm and final). Not too much writing (around 2 pages), no right or wrong answer but put forth your argument. For this semester, the final is like a mini-project (open everything except human interaction). Good course for elective in ML specialization. Code in python (Jupyter notebook). Probably need to spend some time finding a dataset for your project. Active Piazza interaction between students/TA.

The course (Summer 2020) consists of an open-book midterm, a take-home final exam, 5 homework assignments, a final project (individual or group project options), and a dozen or so graded discussion questions. This course requires a beginner's knowledge of python and basic statistics; both the statistics and python portions are covered in lecture.

Overall, this course is pretty easy. The workload is low in the beginning and increases in the last ~4 weeks of the semester. The ideas presented in lecture are easy to grasp and not too technical. The lectures touch on general questions about ethics for AI, AI-related scandals that have been in the news over the last few years, and focuses heavily on bias in algorithms. The more substantial assignments are all in python and focus on mitigating bias in datasets and algorithms, with a major focus on protected classes (race, sex, etc.) The projects are interesting from a data science perspective, and I would say that this course would be good to have for ethical purposes for students who plan to work with AI/ML. However, I would say that this course does not gain you any deep understanding about AI, ML, or algorithms. If you are looking for a prep course for AI or ML, this is not it. Overall, the lecture style was soothing, though not particularly information-dense. The information presented in lecture was related to the homework, so that was a plus.

I would suggest taking this course alongside a harder course.

This is a really interesting class. It's not a hard class from a coding perspective. If you are somewhat familiar with python, pandas, and mathplotlib, the coding assignments would be very simple to accomplish. Additionally, you also get to explore little bit of scikit-learn (linear classifier) as well. I also enjoyed exploring the AIF360 APIs to detect bias via various metrics (Confusion Matrix, Disparate Impact, Statistical Parity, etc) and mitigation techniques (Adversial debasing, Reweighing, Disparate impact remover, etc).

Additionally, the class provided a good intro to descriptive (frequency, standard_dev, mean, mode, variance, correlation, five number summary, etc) and inferential statistics (randomized sampling techniques, Simpson's paradox, etc) concepts. We explored bias and fairness topics such as China's social credit system, or the bias in word embeddings (using word2vec python module), or the bias in the underlying facial recognition libraries.

The recommended text book is Weapons of Math Destruction. Although the text book was not directly used in the lectures or exams, it's a very good book to read to get more insights. The instructor, Professor Howard is very engaged and a really awesome person to interact with. The class lectures are of high quality. The TA team especially Vijay, Jeanette, Bryan were all very helpful. Vijay answered all piazza responses in a record time and was very patient in his responses. The most challenging part for me was the lack of clarity in assignment directions. I think that's because we are more used to having "correct/incorrect" answers in assignments instead of exploring questions that are more abstract where there is no clear right or wrong answer. I highly recommend this class. This class along with ML4T should provide a good beginner level intro to ML.

Overview/Caveat

AI Ethics was brand-new this semester. While I haven't been actively reviewing things in the past, I felt it's important that folks be given an impression of this class. A lot of people have been asking, so I'll try and make this somewhat detailed and go into specifics where possible. I've completed 8 courses in OMSCS as of this writing as well as my undergrad at GT, for reference.

Take this review with a grain of salt; throughout the semester, the teaching team made adjustments to the course based on feedback from students and also implemented some changes given the COVID-19 situation. This flexibility, in general, is a good thing, and it's encouraging to see the teaching staff being responsive and making some needed changes. However, it does mean that my review may not be fully accurate to the course as it continues to adapt; I can only speak to my experience.

I'll start off with good/bad highlights and get into more specifics below.

The Good

• Content was topically relevant. Students were encouraged to read material from ongoing ethics issues, recent news stories, and thought-experiments relevant to today. The material is definitely not abstract/theoretical.

• Course doesn't assume a strong programming / AI background and could therefore serve as a gentle introduction to these topics for folks who are newer.

• Students get exposure to some real examples of algorithmic bias and can see how marginalized groups can be negatively impacted, even in the absence of malicious intent.

The Bad

• Theoretical depth is lacking. Initial assignments are just reporting basic metrics using python/pandas and the later ones rely on third-party libraries which aren't explored from an implementation perspective. The student is on their own to dig into these libraries and see how the underlying algorithms work.

• The exam was poor. Because the first portion of the course covered material which is mostly review for people, the midterm exam was comprised of mostly trick questions and 'gotchas'.

• The participation assignments felt largely superficial. They were the sort of thing which could work well on-campus but hadn't been ironed out into a good online format yet.

Lectures

• The lectures are professionally done and the professor does a good job ensuring material is engaging and fresh. I definitely skimmed through certain parts, however, as they should be review for most people taking this class in this program. The following are a few examples I feel were unnecessary which were covered at some length:

  • mean, median, mode
  • correlation vs. causation
  • empirical rule

• That's not to say all the material was irrelevant. In many cases there was a good balance between current events/real-world examples and concepts from the course. I'd just suggest a bit more technical depth.

Assignments

• Case Studies/ Exercises: These are short exercises designed around a controversial topic, current event, or ethical issue. They seem tailored to facilitate an in-person discussion on a topic where students can debate back-and-forth. Online, however, this fell a little flat. The assignments usually mandated that a student "respond to two others" who had answered the question. Unfortunately most students just did this minimum bar and never replied when someone responded to their post. I hope this would be reworked in the future.

• Written Critiques: These are more-or-less short essays. The idea is that a student should dive in on a topic or issue and write about it in a bit of length. My biggest issue with these was a lack of clear guidance as to how we'd be graded, but there were some thought-provoking questions

• Projects: Several concepts in the course are taught in a more "hands-on" approach, using the projects to showcase concepts. Unfortunately these either are so basic as to be uninteresting to someone with a background in data manipulation in python, or very reliant on specific third-party libraries. This resulted in me not learning too much through the projects, which was a shame. It's cool to see some of the bias mitigation libraries work, but algorithmically I couldn't explain to you how they work in anything more than superficial detail.

The final project is (optionally) a group project. Since grouping was optional I didn't see this as an issue. We were also probably helped out by the fact that most students in the course had been through several OMS courses already and so were not totally incompetent.

Exams

The course is slated to have one midterm exam and one final exam which is not cumulative. This semester, the final exam was dropped and a short mini-project replaced it. The midterm was a bit of a trainwreck; the exam mostly covered some of the basic concepts listed from the lectures above, but attempted to trick students doing silly things like switching units or using subtle language tricks to be misleading. Maybe these concepts are emphasized more heavily on campus, but many students in the online section missed these questions. Thankfully, the teaching team allowed for corrections to be made to recover points.

Grading

The grading for this course was quite lenient. Many of the smaller assignments (case studies etc.) seem to be predominantly graded on completion. My written critiques were also marked with 100%, though I was disappointed I didn't receive any feedback on my responses. Output for the projects is mostly reports with tables of statistics and numeric metrics with some small written portions; these were also graded generously and I never had any of my numbers questioned. Grading for the course was a bit all over the place but the corrections seemed to make up for that. Expect the exam format to evolve.

Last Thoughts

This course has a lot of potential but one could tell it was a first iteration in need of a good polish. The teaching team's willingness to adapt would suggest to me that the course may improve over subsequent semesters. If you're on-the-fence, maybe wait a few semesters and see how things progress, but overall this course is a light-workload introduction to a few technical concepts with a solid grounding in real-world examples.

A new course which was offered in thi semester. It is a machine learning specialization elective. Good introductory course to ML. Kinda like ML4T. First part of the course is mostly about understanding data, statistics basics, predictive algorithms. I found the second part more challenging involving new concepts concerning fairness and bias. The assignments are easy. But since there are many exercises, assignments, written critiques do not underestimate the commitment this course requires. Exams are open book, so we can ease there. The professor and TAs are very understanding and help the students with any doubts you have. Overall a good course which will give you a new perspective of seeing AI.

This semester is the first time this class was offered online and while the class isn't over yet, I know students are registering now so I wanted to leave my impression.

--Overall-- This is a great class. The course content is relevant and curated well. Dr. Howard did a great job giving interesting projects that demonstrated, both in code and in real life, how algorithms can be used to create unfair outcomes and unscientific conclusions. She was also active on Piazza and in the Slack. TAs were responsive and turned around grades promptly.

--Details-- Class consists of weekly discussions, several written critiques, homework projects, a final project and 2 exams. Weekly discussions are focused on ambiguous real world ethical situations related to AI and students are encouraged to provide their opinions on the topic and respond to one another. There's no grade for being "right" or "wrong" but more around how thoughtful the answer is. Written critiques are just that, you have to write about an ethical dilemma in existing AI technology, but in more depth. Homework projects were probably my favorite. The course starts out much easier and slowly eases you into further analysis. The data analysis can be done any way you like but some of the data sets are so large I'd recommend using Python anyways to make it easier on yourself.

--Areas for Improvement-- If I have any complaints, it's only that this course could be made a bit more challenging. Not in the way of busywork; the pacing was perfect. But some early assignments are a bit too simple, and I'd like to see more coursework developed around building bias mitigation algorithms, automatically evaluating bias in an algorithm's output and writing assignments based on current research in the field (perhaps by replicating an existing paper). This type of material was made available for additional reading but wasn't required in assignments.

--Should you take this as your first class?-- That being said, while the course felt easier than other OMSCS courses, I'd hesitate to recommend it as a first class. The course does assume you know a bit about ML already, and it assumes you are a proficient programmer who can figure out how to write your own analysis code. If you're not from a CS background and are looking for a gentler introduction, take ML4T.

Overall, I think for a first class on this topic, this was excellent. I look forward to how the course will be improved over time.