Audit, Don’t Explain – Recommendations Based on a Socio-Technical Understanding of ML-Based Systems

dc.contributor.author	Heuer, Hendrik
dc.contributor.editor	Wienrich, Carolin
dc.contributor.editor	Wintersberger, Philipp
dc.contributor.editor	Weyers, Benjamin
dc.date.accessioned	2021-09-05T18:56:35Z
dc.date.available	2021-09-05T18:56:35Z
dc.date.issued	2021
dc.description.abstract	In this position paper, I provide a socio-technical perspective on machine learning-based systems. I also explain why systematic audits may be preferable to explainable AI systems. I make concrete recommendations for how institutions governed by public law akin to the German TÜV and Stiftung Wartentest can ensure that ML systems operate in the interest of the public.	en
dc.identifier.doi	10.18420/muc2021-mci-ws02-232
dc.identifier.uri	https://dl.gi.de/handle/20.500.12116/37371
dc.language.iso	en
dc.publisher	Gesellschaft für Informatik e.V.
dc.relation.ispartof	Mensch und Computer 2021 - Workshopband
dc.relation.ispartofseries	Mensch und Computer
dc.subject	Algorithmic Bias
dc.subject	Algorithmic Experience
dc.subject	Algorithmic Transparency
dc.subject	Human-Centered Machine Learning
dc.subject	Recommender Systems
dc.subject	Social Media
dc.subject	User Beliefs
dc.title	Audit, Don’t Explain – Recommendations Based on a Socio-Technical Understanding of ML-Based Systems	en
dc.type	Text/Workshop Paper
gi.citation.publisherPlace	Bonn
gi.conference.date	5.-8. September 2021
gi.conference.location	Ingolstadt
gi.conference.sessiontitle	MCI-WS02: UCAI 2021: Workshop on User-Centered Artificial Intelligence
gi.document.quality	digidoc

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