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DTSTART:20220327T030000
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DTSTAMP:20260406T041108Z
UID:1638370800@ist.ac.at
DTSTART:20211201T160000
DTEND:20211201T180000
DESCRIPTION:Speaker: Thorsten Joachims\nhosted by Marco Mondelli\nAbstract:
  Search engines and recommender systems have become the dominant matchmake
 r for a wide range of human endeavors -- from online retail to finding rom
 antic partners. Consequently\, they carry substantial power in shaping mar
 kets and allocating opportunity to the participants. In this talk\, I will
  discuss how the machine learning algorithms underlying these system can p
 roduce unfair ranking policies for both exogenous and endogenous reasons. 
 Exogenous reasons often manifest themselves as biases in the training data
 \, which then get reflected in the learned ranking policy and lead to rich
 -get-richer dynamics. But even when trained with unbiased data\, reasons e
 ndogenous to the algorithms can lead to unfair or undesirable allocation o
 f opportunity. To overcome these challenges\, I will present new machine l
 earning algorithms that directly address both endogenous and exogenous unf
 airness. 
LOCATION:Zoom: https://istaustria.zoom.us/j/92105664264?pwd=UG1icDk0Ym9GYld
 ZZW41VWlOT0JhUT09  Meeting ID: 921 0566 4264 Passcode: 715403\, ISTA
ORGANIZER:
SUMMARY:Thorsten Joachims: Fair Recommendations with Biased Data
URL:https://talks-calendar.ista.ac.at/events/3428
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