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UID:349@isdm.umontpellier.fr
DTSTART;TZID=Europe/Paris:20250619T140000
DTEND;TZID=Europe/Paris:20250619T140000
DTSTAMP:20260828T093257Z
URL:https://isdm.umontpellier.fr/events/can-you-be-fair-and-unaware-a-stud
 y-of-fair-regression-in-the-unaware-framework-2/
SUMMARY:Can you be fair and unaware? A study of fair regression in the unaw
 are framework
DESCRIPTION:Room 02.124\, Building 5\, St Priest campus\n\nMachine Learning
  in Montpellier\, Theory &amp\; Practice - Vincent Divol (ENSAE/CREST)\n\n
 Algorithms are now routinely used to help make decisions in areas like hir
 ing\, school admissions\, and healthcare. These tools can be helpful (they
  are fast and can handle lots of data) but they alsocome with risks. If th
 e data they are trained on reflect unfair patterns from the past\, the alg
 orithms can end up repeating those same patterns. In this talk\, I will in
 troduce a way to think about and improve fairness in algorithms\, called s
 tatistical fairness. I will focus on one idea in particular: demographic p
 arity. This is the idea that an algorithm’s predictions should be spread
  out similarly across different groups (for example\, different genders or
  ethnicities) regardless of other differences between them. I will present
  recent results on regression problems subject to a demographic parity con
 straint. I will focus in particular on the unaware setting\, where the sen
 sitive attribute (such as gender or race) cannot be used in decision-makin
 g—either because it is unavailable or because its use is restricted by l
 aw. Joint work with Solenne Gaucher (CMAP)\n\nMachine Learning in Montpell
 ier\, Theory &amp\; Practice
ATTACH;FMTTYPE=image/jpeg:https://isdm.umontpellier.fr/wp-content/uploads/
 2026/06/ml-mtp-gC78d5.png
CATEGORIES:ML MTP
LOCATION:Room 02.124 Building 5\, St Priest Campus\, Montpellier\, 34000\, 
 France
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 ier\, 34000\, France;X-APPLE-RADIUS=100;X-TITLE=Room 02.124 Building 5:geo
 :0,0
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DTSTART:20250330T030000
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