BEGIN:VCALENDAR METHOD:REQUEST PRODID:Microsoft Exchange Server 2010 VERSION:2.0 BEGIN:VTIMEZONE TZID:GMT Standard Time BEGIN:STANDARD DTSTART:16010101T020000 TZOFFSETFROM:+0100 TZOFFSETTO:+0000 RRULE:FREQ=YEARLY;INTERVAL=1;BYDAY=-1SU;BYMONTH=10 END:STANDARD BEGIN:DAYLIGHT DTSTART:16010101T010000 TZOFFSETFROM:+0000 TZOFFSETTO:+0100 RRULE:FREQ=YEARLY;INTERVAL=1;BYDAY=-1SU;BYMONTH=3 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT ORGANIZER;CN=Daniele Quercia:mailto:[email protected] ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=smartdater [email protected]:mailto:[email protected] ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=members@sm artdata.polito.it:mailto:[email protected] ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=MINDS:mail to:[email protected] ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=HCI:mailto :[email protected] ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=nexa@serve r-nexa.polito.it:mailto:[email protected] DESCRIPTION;LANGUAGE=en-US:Title: BRIO: A Bias and Risk Assessment Tool for Fair ML Systems\nGiuseppe Primiero\, University of Milan\n\nJoin the meet ing <https://teams.microsoft.com/l/meetup-join/19%3ameeting_ZWE1MDUyMWItM2 Q2MC00OTA3LWE1YjktYjYyNmE2OGFlMTky%40thread.v2/0?context=%7b%22Tid%22%3a%2 25d471751-9675-428d-917b-70f44f9630b0%22%2c%22Oid%22%3a%221e405340-2229-45 54-b37f-b193c118d70e%22%7d>\n\n\nAbstract: Phenomena of bias by AI systems based on machine learning methods are well known\, and largely discussed in the literature. A variety of tools are being developed to assess these undesirable behaviours. In this talk I present BRIO\, a bias and risk asse ssment tool developed by MIRAI (https://mirai.systems). The tool is based on a combination of formal and statistical methods and works on the I/O da ta of a ML system remaining agnostic on the model itself. The result of th e analysis is a set of all the features and combinations thereof that prod uce violations with respect to a given target distribution. These values c an be fed into a risk function which computes an overall value weighting t hem on parameters such as size of the population and number of features in volved\, mapping naturally into notions of group and individual fairness.\ n\n<https://arxiv.org/abs/2407.02191>Bio: Giuseppe Primiero is Professor of Logic with the Logic\, Uncertainty\, Computation and Information Lab (l uci.unimi.it) in the Department of Philosophy at the University of Milan\, Italy. He acts as Scientific Director for PHILTECH\, Research Center for The Philosophy of Technology (https://philtech.unimi.it/) and as Programme Leader for the Master's Degree in Human-Centered AI (https://hcai.cdl.uni mi.it/en). He is co-founder and Chief Research Officer of MIRAI (https://m irai.systems). Giuseppe works in the formal modeling and verification of m ulti-agent systems. His preferred tools are proof-systems\, modal and comp utational logics. Giuseppe's formal research is applied to AI systems and their deployment for resolving issues of misinformation and disinformation online\, as well as computable approaches to the evaluation of trustworth iness of information sources\, bias and fairness. He is currently the Prin cipal Investigator of the Projects “BRIO: Bias\, Risk and Opacity in AI ” (https://sites.unimi.it/brio)\, and "SMARTEST: Simulation of probabili stic systems for the age of the digital twin" (https://sites.unimi.it/smar test/) both funded by the Italian Ministry of University and Research. Ful l CV at https://work.unimi.it/chiedove/cv/giuseppe_primiero.pdf.\n\nSubscr ibe to future talk announcements: Anyone outside Bell Labs can receive tal k announcements by subscribing to the mailing list. To subscribe\, send an empty email with the subject line "Subscribe RAI" to daniele.quercia@poli to.it<mailto:[email protected]>\n\n\n\n\n\n UID:040000008200E00074C5B7101A82E008000000009A42AC4AF1EBDB01000000000000000 0100000005E74BBFE92AF0844B57B86088926D2DF SUMMARY;LANGUAGE=en-US:[Responsible AI] BRIO: A Bias and Risk Assessment To ol for Fair ML Systems DTSTART;TZID=GMT Standard Time:20250714T153000 DTEND;TZID=GMT Standard Time:20250714T163000 CLASS:PUBLIC PRIORITY:5 DTSTAMP:20250703T080726Z TRANSP:OPAQUE STATUS:CONFIRMED SEQUENCE:0 X-MICROSOFT-CDO-APPT-SEQUENCE:0 X-MICROSOFT-CDO-OWNERAPPTID:2123833242 X-MICROSOFT-CDO-BUSYSTATUS:TENTATIVE X-MICROSOFT-CDO-INTENDEDSTATUS:BUSY X-MICROSOFT-CDO-ALLDAYEVENT:FALSE X-MICROSOFT-CDO-IMPORTANCE:1 X-MICROSOFT-CDO-INSTTYPE:0 X-MICROSOFT-DONOTFORWARDMEETING:FALSE X-MICROSOFT-DISALLOW-COUNTER:FALSE X-MICROSOFT-REQUESTEDATTENDANCEMODE:DEFAULT X-MICROSOFT-ISRESPONSEREQUESTED:TRUE BEGIN:VALARM DESCRIPTION:REMINDER TRIGGER;RELATED=START:-PT15M ACTION:DISPLAY END:VALARM END:VEVENT END:VCALENDAR
