Investigating sources of algorithmic bias for A.I. / M.L. applications

Algorithmic bias is an increasingly serious societal issue.  It occurs when the outcomes of a software program are biased based on data collected or algorithms created by non-representative groups of humans.  Example: Amazon needed to scrap its "artificial intelligence" based recruiting tool because the selection was shown to be biased against women.  Twitter needed to remove an image cropping feature because of inherent bias against dark skinned people.  Other examples include search engine results and social media platforms.  All of these wonders of "AI" already have significant impact ranging from inadvertent privacy violations to reinforcing social biases of race, gender, sexuality, and ethnicity.  In this research, we are interested to study algorithmic biases that reflect "systematic and unfair" discrimination.