Trustworthy User Modeling and Recommendation from Technical and Regulatory Perspectives

Markus Schedl; Vito Walter Anelli; Elisabeth Lex

doi:10.1145/3631700.3658522

Trustworthy User Modeling and Recommendation from Technical and Regulatory Perspectives

Markus Schedl, Vito Walter Anelli, Elisabeth Lex

Institute of Human-Centred Computing (7060)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Abstract

This tutorial provides an interdisciplinary overview of fairness, non-discrimination, transparency, privacy, and security in the context of recommender systems. According to European policies, these are essential dimensions of trustworthy AI systems but also extend to the global debate on regulating AI technology. Since the aspects mentioned earlier require more than technical considerations, we discuss these topics from ethical, legal, and regulatory perspectives. While the tutorial's primary focus is on presenting technical solutions that address the mentioned topics of trustworthiness, it also equips the primarily technical audience of UMAP with the necessary understanding of the social and ethical implications of their research and development and recent ethical guidelines and regulatory frameworks.

Original language	English
Title of host publication	UMAP 2024 - Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization
Publisher	Association of Computing Machinery
Pages	17-19
Number of pages	3
ISBN (Electronic)	9798400704666
DOIs	https://doi.org/10.1145/3631700.3658522
Publication status	Published - 27 Jun 2024
Event	32nd ACM Conference on User Modeling, Adaptation and Personalization: UMAP 2024 - Cagliari, Italy Duration: 1 Jul 2024 → 4 Jul 2024 https://www.um.org/umap2024/

Conference

Conference	32nd ACM Conference on User Modeling, Adaptation and Personalization
Abbreviated title	ACM UMAP 2024
Country/Territory	Italy
City	Cagliari
Period	1/07/24 → 4/07/24
Internet address	https://www.um.org/umap2024/

Keywords

diversity
ethics
explainability
fairness
learning to rank
non-discrimination
privacy
ranking models
recommender systems
regulation
security
transparency

ASJC Scopus subject areas

Software

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10.1145/3631700.3658522Licence: CC BY-NC-ND 4.0

Cite this

Trustworthy User Modeling and Recommendation from Technical and Regulatory Perspectives. / Schedl, Markus; Anelli, Vito Walter; Lex, Elisabeth.
UMAP 2024 - Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization. Association of Computing Machinery, 2024. p. 17-19.

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Schedl, M, Anelli, VW & Lex, E 2024, Trustworthy User Modeling and Recommendation from Technical and Regulatory Perspectives. in UMAP 2024 - Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization. Association of Computing Machinery, pp. 17-19, 32nd ACM Conference on User Modeling, Adaptation and Personalization, Cagliari, Italy, 1/07/24. https://doi.org/10.1145/3631700.3658522

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Trustworthy User Modeling and Recommendation from Technical and Regulatory Perspectives

Abstract

Conference

Keywords

ASJC Scopus subject areas

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