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 fairness, we design a novel technique for verifying probabilistic properties that admits a wide class of decision-making programs. Third, we present FairSquare, the first verification tool for automatically certifying that a program meets a given fairness property. We evaluate FairSquare on a range of decision-making programs. Our evaluation demonstrates FairSquare’s ability to verify fairness for a range of different programs, which we show are out-of-reach for state-of-the-art program analysis techniques.
Thu 26 OctDisplayed time zone: Tijuana, Baja California change
13:30 - 15:00 | |||
13:30 22mTalk | Seam: Provably Safe Local Edits on Graphs OOPSLA Manolis Papadakis Stanford University, USA, Gilbert Louis Bernstein Stanford University, USA, Rahul Sharma Microsoft Research, Alex Aiken Stanford University, Pat Hanrahan Stanford University, USA DOI | ||
13:52 22mTalk | TiML: A Functional Language for Practical Complexity Analysis with Invariants OOPSLA Peng Wang Massachusetts Institute of Technology, USA, Di Wang Peking University, China, Adam Chlipala Massachusetts Institute of Technology, USA DOI | ||
14:15 22mTalk | FairSquare: Probabilistic Verification of Program Fairness OOPSLA Aws Albarghouthi University of Wisconsin-Madison, Loris D'Antoni University of Wisconsin–Madison, Samuel Drews University of Wisconsin-Madison, Aditya Nori DOI | ||
14:37 22mTalk | Reasoning on Divergent Computations with Coaxioms OOPSLA DOI | ||