RT Book
T1 Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions
A1 Barocas, Solon
A2 Potash, Eric
A2 Mitchell, Shira
A2 Lum, Kristian
A2 D'Amour, Alexander
LA English
YR 2020
UL https://krimdok.uni-tuebingen.de/Record/1865807133
AB A recent flurry of research activity has attempted to quantitatively define "fairness" for decisions based on statistical and machine learning (ML) predictions. The rapid growth of this new field has led to wildly inconsistent terminology and notation, presenting a serious challenge for cataloguing and comparing definitions. This paper attempts to bring much-needed order. First, we explicate the various choices and assumptions made---often implicitly---to justify the use of prediction-based decisions. Next, we show how such choices and assumptions can raise concerns about fairness and we present a notationally consistent catalogue of fairness definitions from the ML literature. In doing so, we offer a concise reference for thinking through the choices, assumptions, and fairness considerations of prediction-based decision systems
K1 Research