BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//cfp.cttue.de//tdf5//PFP7QW
BEGIN:VTIMEZONE
TZID:CET
BEGIN:STANDARD
DTSTART:20001029T040000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10
TZNAME:CET
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20000326T030000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3
TZNAME:CEST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:pretalx-tdf5-SBXZNK@cfp.cttue.de
DTSTART;TZID=CET:20260517T133000
DTEND;TZID=CET:20260517T141500
DESCRIPTION:Algorithmic predictions are used to allocate social goods such 
 as healthcare\, job training\, and education. Despite efforts to apply fai
 rness frameworks and participatory approaches\, practical outcomes remain 
 problematic as recent investigations have shown. This talk examines standa
 rd approaches to ‘fair machine learning’ through three cases: (1) heal
 th programs\, (2) long-term unemployment\, and (3) school dropout. It crit
 ically assesses their limitations and normative assumptions. Two key disti
 nctions clarify the debates: fairness-focused versus welfare-focused metho
 ds on the one hand\, and whether predictions are instrumentally or communi
 catively rational on the other. The latter distinction stresses whether al
 gorithms serve effective implementation or facilitate collective evaluatio
 n of policy goals.
DTSTAMP:20260530T143438Z
LOCATION:BOOL
SUMMARY:Computer says no. Troubles with fixing algorithmic decision-making.
  - Sebastian Zezulka
URL:https://cfp.cttue.de/tdf5/talk/SBXZNK/
END:VEVENT
END:VCALENDAR