BEGIN:VCALENDAR VERSION:2.0 PRODID:-//UC Irvine//CML Seminars//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:The Measurement and Mismeasurement of Trustworthy ML X-WR-TIMEZONE:America/Los_Angeles BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20070311T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20071104T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:2021-04-12-sanmi-koyejo@cml.ics.uci.edu DTSTAMP:20210412T000000Z SEQUENCE:57690 DTSTART;TZID=America/Los_Angeles:20210412T130000 DTEND;TZID=America/Los_Angeles:20210412T140000 SUMMARY:[CML Seminar] Sanmi Koyejo: The Measurement and Mismeasurement of T rustworthy ML LOCATION:Online (live stream) DESCRIPTION:Sanmi Koyejo\, Assistant Professor\, Department of Computer Sci ence\, University of Illinois at Urbana-Champaign\n\nTitle: The Measuremen t and Mismeasurement of Trustworthy ML\n\nAbstract: Across healthcare\, sc ience\, and engineering\, we increasingly employ machine learning to autom ate decision-making that affects our lives in profound ways. However\, ML can fail\, and reliably measuring such failures is the first step toward b uilding trustworthy learning machines. Consider algorithmic fairness\, whe re widely-deployed fairness metrics can exacerbate group disparities and a re often incompatible. Measurement is also crucial for robustness\, partic ularly in federated learning with error-prone devices. Across ML applicati ons\, the dire consequences of mismeasurement are a recurring theme. This talk outlines emerging strategies for addressing the measurement gap in ML and how this impacts trustworthiness.\n\nhttps://cml.ics.uci.edu/seminars /2021-04-12-sanmi-koyejo X-ALT-DESC;FMTTYPE=text/html:Sanmi Koyejo\, Assistant Pr ofessor\, Department of Computer Science\, University of Illinois at Urban a-Champaign

Title: The Measurement and Mismeasurement of Tru stworthy ML

Abstract: Across healthcare\, science\, and engi neering\, we increasingly employ machine learning to automate decision-mak ing that affects our lives in profound ways. However\, ML can fail\, and r eliably measuring such failures is the first step toward building trustwor thy learning machines. Consider algorithmic fairness\, where widely-deploy ed fairness metrics can exacerbate group disparities and are often incompa tible. Measurement is also crucial for robustness\, particularly in federa ted learning with error-prone devices. Across ML applications\, the dire c onsequences of mismeasurement are a recurring theme. This talk outlines em erging strategies for addressing the measurement gap in ML and how this im pacts trustworthiness.

https://cml.ics.uci.edu/seminars/2021-04-12-sanmi-k oyejo URL:https://cml.ics.uci.edu/seminars/2021-04-12-sanmi-koyejo END:VEVENT END:VCALENDAR