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Towards algorithm auditing: managing legal, ethical and technological risks of AI, ML and associated algorithms

­Business reliance on algorithms is becoming ubiquitous, and companies are increasingly concerned about their algorithms causing major financial or reputational damage. High-profile cases include Google’s AI algorithm for photo classification mistakenly labelling a black couple as gorillas in 2015 (...

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Principais autores: Adriano Koshiyama, Emre Kazim, Philip Treleaven, Pete Rai, Lukasz Szpruch, Giles Pavey, Ghazi Ahamat, Franziska Leutner, Randy Goebel, Andrew Knight, Janet Adams, Christina Hitrova, Jeremy Barnett, Parashkev Nachev, David Barber, Tomas Chamorro-Premuzic, Konstantin Klemmer, Miro Gregorovic, Shakeel Khan, Elizabeth Lomas, Airlie Hilliard, Siddhant Chatterjee
Format: Artigo
Sprog:Inglês
Udgivet: The Royal Society 2024-05-01
Serier:Royal Society Open Science
Fag:
artificial intelligence
machine learning
explainability
auditing
bias
transparency
Online adgang:https://royalsocietypublishing.org/doi/10.1098/rsos.230859
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