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 […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"ACM","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"OOPSLA '17: OO Programming, Systems, Languages, and 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