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hello and welcome to module on choices and fairness criteria my name is mike yoderescu iamp;#39;m assistant professor information systems at boston college as well as a visiting scholar at mitd lab weamp;#39;ll cover this module will be the concept of confusion matrix as well as three popular examples of fairness criteria demographic parity equalized odds and equals opportunity some countries have laws that protect specific groups of people from discrimination based on certain attributes as we review in the previous video these are called protected attributes some examples are on the slide regardless of a legal landscape machine learning has the potential to produce unfair outcomes for certain groups of people as an algae designer one should make clear choice about fairness criteria some criteria will be reviewed in the next few slides the previous video we also discussed cases of fairness to unawareness which refers to leaving out the protect attributes out of your model and
