"they're almost never program requirements" Wrong. We had two courses at my school. From the ABET Criteria for Accrediting Engineering Technology Programs:

"they're almost never program requirements" Wrong. We had two courses at my school. From the ABET Criteria for Accrediting Engineering Technology Programs: "(e)Include topics related to professional and ethical responsibilities". The issue here is proxy features, and that it isnt as obvious if your statistical model is doing something wrong. You identified zip code and race. What about number of cats? Would you have a particular gender and marital status in mind if a given data sample had a large value in the numberOfCats column? So you ban making decisions based on number of cats. But amountOfKittyLitterPurchased, numberOfCatPicturesViewed, numberOfDislikesOnDogPictures, etc could be used as a proxy for numberOfCats. Try to come up with an exhaustive list of all features which could be used as a proxy for some protected class. Starting with politically incorrect jokes will get you part way there, assuming you possess an exhaustive list of all politically incorrect jokes. But you will need to be pretty creative to come up with more. https://www.abet.org/accreditation/accreditation-criteria/criteria-for-accrediting-engineering-technology-programs-2018-2019/#GC5 https://www.abet.org/accreditation/accreditation-criteria/cr...