SSA Victoria Branch presents: Algorithmic Fairness: Choices, Assumptions, and Definitions by Shira Mitchell.
A recent wave of research has attempted to define fairness quantitatively. In particular, this work has explored what fairness might mean in the context of decisions based on the predictions of statistical and machine learning models. The rapid growth of this new field has led to wildly inconsistent motivations, terminology, and notation, presenting a serious challenge for cataloging and comparing definitions. This article attempts to bring much-needed order. First, we explicate the various choices and assumptions made—often implicitly—to justify the use of prediction-based decision-making. Next, we show how such choices and assumptions can raise fairness concerns and we present a notationally consistent catalog of fairness definitions from the literature. In doing so, we offer a concise reference for thinking through the choices, assumptions, and fairness considerations of prediction-based decision-making.
Presenter: Shira Mitchell is a statistician working in government and politics. After her PhD at Harvard and postdoc at Columbia, worked at Mathematica Policy Research on small area estimation and causal inference for federal agencies. She then worked at the NYC Mayor’s Office of Data Analytics. Now she is a statistician at Blue Rose Research, doing polling and message testing for the progressive left.
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