Exploring the Role of Local and Global Explanations in Recommender Systems

Marissa Radensky; Doug Downey; Kyle Lo; Zoran Popovic; Daniel S. Weld

doi:10.1145/3491101.3519795

Exploring the Role of Local and Global Explanations in Recommender Systems

Marissa Radensky, Doug Downey, Kyle Lo, Zoran Popovic, Daniel S. Weld

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Scopus citations

Abstract

Explanations are well-known to improve recommender systems' transparency. These explanations may be local, explaining individual recommendations, or global, explaining the recommender model overall. Despite their widespread use, there has been little investigation into the relative benefits of the two explanation approaches. We conducted a 30-participant exploratory study and a 30-participant controlled user study with a research-paper recommender to analyze how providing local, global, or both explanations influences user understanding of system behavior. Our results provide evidence suggesting that both are more helpful than either alone for explaining how to improve recommendations, yet both appeared less helpful than global alone for efficiently identifying false positive and negative recommendations. However, we note that the two explanation approaches may be better compared in a higher-stakes or more opaque domain.

Original language	English (US)
Title of host publication	CHI 2022 - Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450391566
DOIs	https://doi.org/10.1145/3491101.3519795
State	Published - Apr 27 2022
Event	2022 CHI Conference on Human Factors in Computing Systems, CHI EA 2022 - Virtual, Online, United States Duration: Apr 30 2022 → May 5 2022

Publication series

Name	Conference on Human Factors in Computing Systems - Proceedings

Conference

Conference	2022 CHI Conference on Human Factors in Computing Systems, CHI EA 2022
Country/Territory	United States
City	Virtual, Online
Period	4/30/22 → 5/5/22

Funding

This research was supported by the University of Washington WRF/Cable Professorship and the Allen Institute for Artifcial Intelligence (AI2). The authors thank Matt Latzke and Cecile Nguyen for their advice regarding the project’s user-interface design and code respectively. The authors thank all the participants and pilot participants who made this work possible. Finally, the authors thank the anonymous reviewers of this work for their valuable feedback.

Keywords

explainable AI
human-AI interaction

ASJC Scopus subject areas

Human-Computer Interaction
Computer Graphics and Computer-Aided Design
Software

Access to Document

10.1145/3491101.3519795

Cite this

Radensky, M., Downey, D., Lo, K., Popovic, Z., & Weld, D. S. (2022). Exploring the Role of Local and Global Explanations in Recommender Systems. In CHI 2022 - Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems Article 290 (Conference on Human Factors in Computing Systems - Proceedings). Association for Computing Machinery. https://doi.org/10.1145/3491101.3519795

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abstract = "Explanations are well-known to improve recommender systems' transparency. These explanations may be local, explaining individual recommendations, or global, explaining the recommender model overall. Despite their widespread use, there has been little investigation into the relative benefits of the two explanation approaches. We conducted a 30-participant exploratory study and a 30-participant controlled user study with a research-paper recommender to analyze how providing local, global, or both explanations influences user understanding of system behavior. Our results provide evidence suggesting that both are more helpful than either alone for explaining how to improve recommendations, yet both appeared less helpful than global alone for efficiently identifying false positive and negative recommendations. However, we note that the two explanation approaches may be better compared in a higher-stakes or more opaque domain.",

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Radensky, M, Downey, D, Lo, K, Popovic, Z & Weld, DS 2022, Exploring the Role of Local and Global Explanations in Recommender Systems. in CHI 2022 - Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems., 290, Conference on Human Factors in Computing Systems - Proceedings, Association for Computing Machinery, 2022 CHI Conference on Human Factors in Computing Systems, CHI EA 2022, Virtual, Online, United States, 4/30/22. https://doi.org/10.1145/3491101.3519795

Exploring the Role of Local and Global Explanations in Recommender Systems. / Radensky, Marissa; Downey, Doug; Lo, Kyle et al.
CHI 2022 - Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, 2022. 290 (Conference on Human Factors in Computing Systems - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Exploring the Role of Local and Global Explanations in Recommender Systems

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