“…Techniques for diminishing technical definitions of bias and unfairness have been developed by corporate (Zhang, Lemoine, & Mitchell, 2018), scholarly (Kearns, Roth, & Wu, 2017), and civil society actors (Duarte, 2017). These efforts have historical parallels in the social sciences, particularly around quantitative educational, vocational (Hutchinson & Mitchell, 2019), and psychometric testing (Lussier, 2018). Fairness, Accountability, and Transparency in Machine Learning (FATML) workshops, held yearly in conjunction with the International Conference on Machine Learning (ICML) from 2014 to 2018, were organized by a group comprised largely of computer scientists.…”