BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Miguel Couceiro (Université de Lorraine) DTSTART:20210203T180000Z DTEND:20210203T190000Z DTSTAMP:20260423T003241Z UID:MPML/34 DESCRIPTION:Title: Ma king ML Models fairer through explanations\, feature dropout\, and aggrega tion\nby Miguel Couceiro (Université de Lorraine) as part of Mathemat ics\, Physics and Machine Learning (IST\, Lisbon)\n\n\nAbstract\nAlgorithm ic decisions are now being used on a daily basis\, and based on Machine Le arning (ML) processes that may be complex and biased. This raises several concerns given the critical impact that biased decisions may have on indiv iduals or on society as a whole. Not\nonly unfair outcomes affect human ri ghts\, they also undermine public trust in ML and AI. In this talk\, we wi ll address fairness issues of ML models based on decision outcomes\, and w e will show how the simple idea of "feature dropout" followed by an "ensem ble approach" can improve model fairness without compromising its accuracy . To illustrate we will present a general workflow that relies on explaine rs to tackle "process fairness"\, which essentially measures a model's rel iance on sensitive or discriminatory features. We will present different a pplications and empirical settings that show improvements not only with re spect to process fairness but also other fairness metrics.\n LOCATION:https://researchseminars.org/talk/MPML/34/ END:VEVENT END:VCALENDAR