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DESCRIPTION:Taming the Machines\n\nAI\, Explainability and Epistemic Depend
 ence\n\nConférence donnée par Jocelyn Maclure à l’Université de Hambourg\n
 \nThe idea that people subjected to opaque AI-based decisions have a “righ
 t to explanation”\, under specific circumstances\, is generating a stimula
 ting and productive debate in philosophy. Some early normative defenses of
  the right to explanation or public justifications (Vredenburgh 2021\; Mac
 lure 2021) are being challenged from a variety of perspectives (Ross 2022\
 ; Taylor 2024\; Fritz 2024\; Karlan & Kugelberg 2025). Alternatively\, som
 e are qualifying or refining the case for a right to explanation (Da Silva
  2023\; Grote & Paulo 2025\; Dishaw 2025). While I addressed the argument 
 according to which deep artificial neural networks are not significantly m
 ore fallible and opaque than human minds in a previous paper (2021)\, I no
 w want to turn my attention to two new emerging counterarguments to the ri
 ght to explanation thesis. The first one is normative: the standards of pu
 blic reason do not typically apply to AI decisions and the interests at pl
 ay do not justify the cost of a granting a right to explanation. The secon
 d one is epistemic: social epistemologists have long been urging us to rec
 ognize human thinkers’ basic epistemic dependence upon the testimonies of 
 others and upon a variety of complex social processes. The defenders of th
 e right to explanation arguably overlook the possibility that it may be ju
 stified to defer epistemically to black box algorithms. Although serious\,
  I will argue that these counterarguments are unsuccessful.\n
DTSTART:20260107T231500Z
DTEND:20260108T004500Z
LOCATION:DE\, Universität Hamburg
SUMMARY:AI\, Explainability and Epistemic Dependence
URL:/jarislowsky-chair/fr/channels/event/ai-explainabi
 lity-and-epistemic-dependence-370278
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