Multiperspective and Multidisciplinary Treatment of Fairness in Recommender Systems Research

Markus Schedl; Navid Rekabsaz; Elisabeth Lex; Tessa Grosz; Elisabeth Greif

doi:10.1145/3511047.3536400

Multiperspective and Multidisciplinary Treatment of Fairness in Recommender Systems Research

Markus Schedl, Navid Rekabsaz, Elisabeth Lex, Tessa Grosz, Elisabeth Greif

Institute of Interactive Systems and Data Science (7060)

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

Abstract

In the communities of UMAP, RecSys, and similar venues, fairness of recommender systems has primarily been addressed from the perspective of computer science and artificial intelligence, e.g., by devising computational bias and fairness metrics or elaborating debiasing algorithms. In contrast, we advocate taking a multiperspective and multidisciplinary viewpoint to complement this technical perspective. This involves considering the variety of stakeholders in the value chain of recommender systems as well as interweaving expertise from various disciplines, in particular, computer science, law, ethics, sociology, and psychology (e.g., studying discrepancies between computational metrics of bias and fairness and their actual human perception, and considering the legal and regulatory context recommender systems are embedded in).

Original language	English
Title of host publication	UMAP2022 - Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization
Publisher	Association of Computing Machinery
Pages	90-94
Number of pages	5
ISBN (Electronic)	9781450392327
DOIs	https://doi.org/10.1145/3511047.3536400
Publication status	Published - 4 Jul 2022
Event	30th ACM Conference on User Modeling, Adaptation and Personalization: UMAP 2022 - Virtual, Online, Spain Duration: 4 Jul 2022 → 7 Jul 2022

Conference

Conference	30th ACM Conference on User Modeling, Adaptation and Personalization
Abbreviated title	UMAP2022
Country/Territory	Spain
City	Virtual, Online
Period	4/07/22 → 7/07/22

ASJC Scopus subject areas

Software

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10.1145/3511047.3536400

Cite this

Multiperspective and Multidisciplinary Treatment of Fairness in Recommender Systems Research. / Schedl, Markus; Rekabsaz, Navid; Lex, Elisabeth et al.
UMAP2022 - Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization. Association of Computing Machinery, 2022. p. 90-94.

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

Schedl, M, Rekabsaz, N, Lex, E, Grosz, T & Greif, E 2022, Multiperspective and Multidisciplinary Treatment of Fairness in Recommender Systems Research. in UMAP2022 - Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization. Association of Computing Machinery, pp. 90-94, 30th ACM Conference on User Modeling, Adaptation and Personalization, Virtual, Online, Spain, 4/07/22. https://doi.org/10.1145/3511047.3536400

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abstract = "In the communities of UMAP, RecSys, and similar venues, fairness of recommender systems has primarily been addressed from the perspective of computer science and artificial intelligence, e.g., by devising computational bias and fairness metrics or elaborating debiasing algorithms. In contrast, we advocate taking a multiperspective and multidisciplinary viewpoint to complement this technical perspective. This involves considering the variety of stakeholders in the value chain of recommender systems as well as interweaving expertise from various disciplines, in particular, computer science, law, ethics, sociology, and psychology (e.g., studying discrepancies between computational metrics of bias and fairness and their actual human perception, and considering the legal and regulatory context recommender systems are embedded in).",

author = "Markus Schedl and Navid Rekabsaz and Elisabeth Lex and Tessa Grosz and Elisabeth Greif",

note = "Funding Information: This work received financial support by the Austrian Science Fund (FWF): P33526 and DFH-23; and by the State of Upper Austria and the Federal Ministry of Education, Science, and Research, through grant LIT-2020-9-SEE-113 and LIT-2021-YOU-215. Publisher Copyright: {\textcopyright} 2022 Owner/Author.; 30th ACM Conference on User Modeling, Adaptation and Personalization : UMAP 2022, UMAP2022 ; Conference date: 04-07-2022 Through 07-07-2022",

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N1 - Funding Information: This work received financial support by the Austrian Science Fund (FWF): P33526 and DFH-23; and by the State of Upper Austria and the Federal Ministry of Education, Science, and Research, through grant LIT-2020-9-SEE-113 and LIT-2021-YOU-215. Publisher Copyright: © 2022 Owner/Author.

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ER -

Multiperspective and Multidisciplinary Treatment of Fairness in Recommender Systems Research

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