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DTSTART:20260329T030000
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DTSTAMP:20260529T165842Z
UID:1781607600@ist.ac.at
DTSTART:20260616T130000
DTEND:20260616T140000
DESCRIPTION:Speaker: Konstantin Kueffner\nhosted by Krzysztof Pietrzak\nAbs
 tract: As automated decision-makers have become ubiquitous in many domains
  of life\, their decisions have become increasingly consequential. Recent 
 years have shown that such systems can exhibit discriminatory behaviour ag
 ainst individuals and social groups alike\, thereby amplifying existing bi
 ases and entrenching socio-economic disparities over time. Algorithmic fai
 rness addresses this problem by developing methods to quantify and mitigat
 e unfair behaviour. However\, much of the existing literature studies fair
 ness in a static pre-deployment setting and\, therefore\, neglects that au
 tomated decision-makers are often deployed in dynamic environments\, where
  their behaviour and the populations they affect may change over time.This
  thesis addresses this gap through the lens of runtime verification. Inste
 ad of treating fairness as a property of a classifier together with a fixe
 d input distribution\, it reframes fairness as a property of the interacti
 on trace between the decision-maker and its deployment environment. To eva
 luate such sequential fairness properties\, the thesis develops runtime mo
 nitors that observe the evolving interaction between the system and the en
 vironment and issue verdicts after each new observation. Because\, these m
 onitors are designed to detect unfair behaviour during deployment\, they c
 omplement fair training\, auditing\, verification\, and enforcement by pr
 oviding an additional layer of mathematically rigorous fairness assurance.
 In summary\, the thesis develops quantitative\, trace-based analogues of c
 lassical group and individual fairness measures and constructs monitors fo
 r them. This includes monitors for long-run group fairness over Markovian 
 traces\, for the time-varying welfare of a changing population in a dynami
 cal system\, and for the individual fairness of an arbitrary system genera
 ting a trace of inputs and outputs. To achieve this\, the monitors combine
  ideas from runtime verification\, sequential statistics\, and nearest-nei
 ghbour search. In the group-fairness settings\, monitoring is primarily a 
 sequential statistical estimation problem: the monitor must construct stat
 istically sound interval estimates of fairness values from dependent and p
 artially observed interactions. In the individual-fairness setting\, the m
 ain challenge is computational efficiency: the monitor must detect individ
 ual fairness violations by efficiently comparing the current decision with
  all previously observed decisions.
LOCATION:Central Bldg / O1 / Mondi 3 (I01.O1.010) and Zoom\, ISTA
ORGANIZER:
SUMMARY:Konstantin Kueffner: Thesis Defense: Monitoring Algorithmic Fairnes
 s in Sequential Decision Making
URL:https://talks-calendar.ista.ac.at/events/6480
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