Equality, Equity, and Algorithms: Learning from Justice Rosalie Abella&DAG;

In the United States, employers, schools, and governments use race or other protected classifications can face a collision between two competing legal requirements: to avoid race-conscious decision making and to avoid decisions with racially disparate impacts. Growing use of machine learning and other predictive algorithmic tools heightened this tension as employers and other actors use tools that make choices about contrasting definitions of equality and anti-discrimination; design algorithmic practices against explicit or implicit uses of certain personal characteristics associated with historic discrimination; and address inaccuracies and biases in the data and algorithmic practices. Justice Rosalie Abella's approach to equality issues, highly influential in Canadian law, offers guidance by directing decision makers to (a) acknowledge and accommodate differences in people's circumstances and identities; (b) resist attributing to personal choice the patterns and practices of society, including different starting points and opportunities; and (c) resisting consideration of race or other group identities as justification when used to harm historically disadvantaged groups, but permitting such consideration when intended to remedy historic exclusions or economic disadvantages.