With the range and sensitivity of algorithmic decisions expanding at a break-neck speed, it is imperative that
\nwe aggressively investigate fairness and bias in decision-making programs. First, we show that a number of
\nrecently proposed formal definitions of fairness can be encoded as probabilistic program properties. Second,
\nwith the goal of enabling rigorous reasoning about fairness, we design a novel technique for verifying
\nprobabilistic properties that admits a wide class of decision-making programs. Third, we present FairSquare,
\nthe first verification tool for automatically certifying that a program meets a given fairness property. We
\nevaluate FairSquare on a range of decision-making programs. Our evaluation demonstrates FairSquare\u2019s
\nability to verify fairness for a range of different programs, which we show are out-of-reach for state-of-the-art
\nprogram analysis techniques.<\/p>\n","protected":false},"excerpt":{"rendered":"
With the range and sensitivity of algorithmic decisions expanding at a break-neck speed, it is imperative that we aggressively investigate fairness and bias in decision-making programs. First, we show that a number of recently proposed formal definitions of fairness can be encoded as probabilistic program properties. Second, with the goal of enabling rigorous reasoning about 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