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DTSTART;VALUE=DATE:20220518T160000
DTEND;VALUE=DATE:20220518T160000
UID:11345@agenda.unifr.ch
DESCRIPTION:Despite their impressive performance, machine learning systems remain prohibitively unreliable in safety-, trust-, and ethically sensitive domains. Recent discussions in different sub-fields of AI have reached the consensus of knowledge need in machine learning; few discussions have touched upon the diagnosis of what knowledge is needed. In this talk, I will present our ongoing work on ARCH, a knowledge-driven, human-centered, and reasoning-based tool, for diagnosing the unknowns of a machine learning system. ARCH leverages human intelligence to create domain knowledge required for a given task and to describe the internal behavior of a machine learning system; it infers the missing or incorrect knowledge of the system with the built-in probabilistic, abductive reasoning engine. ARCH is a generic tool that can be applied to machine learning in different contexts. In the talk, I will present several applications in which ARCH is currently being developed and tested, including health, finance, transport, and e-commerce.
SUMMARY:ARCH: Know What Your Machine Doesn’t Know
CATEGORIES:Colloque / Congrès / Forum
LOCATION:PER 21\, C130\, Bd de Pérolles 90\, 1700 Fribourg
URL;VALUE=URI:https://agenda.unifr.ch/e/fr/11345
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