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Building User Trust in Recommendations via Fairness and Explanations

Author:

Dimitris SacharidisAuthors Info & Claims

UMAP '20 Adjunct: Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization

Pages 313 - 314

https://doi.org/10.1145/3386392.3399995

Published: 13 July 2020 Publication History

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Abstract

Modern Artificial Intelligence (AI) techniques, based on the statistical analysis of big volumes of data, are quickly gaining traction across various domains. Recommender Systems are a class of AI techniques that extract preference patterns from large traces of human behavior. Recommenders assist people in taking decisions that range from harmless everyday life dilemmas, e.g., what shoes to buy, to seemingly innocuous choices but with long-term, hidden consequences, e.g., what news article to read, up to more critical decisions, e.g., which person to hire.

As more and more aspects of our everyday lives are influenced by automated decisions made by recommender systems, it becomes natural to question whether these systems are trustworthy, particularly given the opaqueness and complexity of their internal workings. These questions are timely posed in the broader context of concerns regarding the societal and ethical implications of applying AI techniques, which have also brought about new regulations, like the EU's "Right to Explanation".

In this talk, we discuss techniques for increasing the user's trust in the decisions of a recommender system, focusing on fairness aspects and explanation approaches. On the one hand, fairness means that the system exhibits certain desirable ethical traits, such as being non-discriminatory, diversity-aware, and bias-free. On the other hand, explanations provide human-understandable interpretations of the inner working of the system. Both mechanisms can be used in tandem to promote trust in the system. In addition, we investigate user trust from the standpoint of different stakeholders that potentially have varying levels of technical background and diverse needs.

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References

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Robin Burke. 2017. Multisided Fairness for Recommendation. CoRR, Vol. abs/1707.00093 (2017).

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Cited By

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Richa Bedi PGupta SKumar R(2024)Incorporation of Two-Fold Trust in Group Recommender System to Handle Popularity BiasSN Computer Science10.1007/s42979-023-02576-55:2Online publication date: 10-Feb-2024
https://doi.org/10.1007/s42979-023-02576-5
Danemayer JHolloway CCho YBerthouze NSingh ABhot WDixon OGrobelnik MShawe-Taylor J(2023)Seeking information about assistive technologyInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2023.103078177:COnline publication date: 26-Jul-2023
https://dl.acm.org/doi/10.1016/j.ijhcs.2023.103078
Ren WWang LLiu KGuo RPeng LFu Y(2022)Mitigating Popularity Bias in Recommendation with Unbalanced Interactions: A Gradient Perspective2022 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM54844.2022.00054(438-447)Online publication date: Nov-2022
https://doi.org/10.1109/ICDM54844.2022.00054
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Index Terms

Building User Trust in Recommendations via Fairness and Explanations
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

Recommendations

Let Me Explain: Impact of Personal and Impersonal Explanations on Trust in Recommender Systems
CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

Trust in a Recommender System (RS) is crucial for its overall success. However, it remains underexplored whether users trust personal recommendation sources (i.e. other humans) more than impersonal sources (i.e. conventional RS), and, if they do, ...
On the Negative Perception of Cross-domain Recommendations and Explanations
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

Recommender systems typically operate within a single domain, for example, recommending books based on users' reading habits. If such data is unavailable, it may be possible to make cross-domain recommendations and recommend books based on user ...
LLM-generated Explanations for Recommender Systems
UMAP Adjunct '24: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization

Users are often confronted with situations where they have to decide in favor or against an offered item, like a book, movie, or recipe. Those suggested items are commonly determined by a recommender system, which considers personal preferences to ...

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Published In

UMAP '20 Adjunct: Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization

July 2020

395 pages

ISBN:9781450379502

DOI:10.1145/3386392

Editors:
Tsvi Kuflik
University of Haifa, Israel
,
Ilaria Torre
University of Genoa, Italy
,
Robin Burke
University of Colorado, Boulder, USA
,
Cristina Gena
University of Turin, Italy

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2020

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Keynote

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UMAP '20

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UMAP '20: 28th ACM Conference on User Modeling, Adaptation and Personalization

July 14 - 17, 2020

Genoa, Italy

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Overall Acceptance Rate 162 of 633 submissions, 26%

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UMAP '25

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33rd ACM Conference on User Modeling, Adaptation and Personalization

June 16 - 19, 2025

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Cited By

View all

Richa Bedi PGupta SKumar R(2024)Incorporation of Two-Fold Trust in Group Recommender System to Handle Popularity BiasSN Computer Science10.1007/s42979-023-02576-55:2Online publication date: 10-Feb-2024
https://doi.org/10.1007/s42979-023-02576-5
Danemayer JHolloway CCho YBerthouze NSingh ABhot WDixon OGrobelnik MShawe-Taylor J(2023)Seeking information about assistive technologyInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2023.103078177:COnline publication date: 26-Jul-2023
https://dl.acm.org/doi/10.1016/j.ijhcs.2023.103078
Ren WWang LLiu KGuo RPeng LFu Y(2022)Mitigating Popularity Bias in Recommendation with Unbalanced Interactions: A Gradient Perspective2022 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM54844.2022.00054(438-447)Online publication date: Nov-2022
https://doi.org/10.1109/ICDM54844.2022.00054
Schelenz L(2021)Diversity-aware Recommendations for Social Justice? Exploring User Diversity and Fairness in Recommender SystemsAdjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450614.3463293(404-410)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3450614.3463293

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Index Terms

Recommendations

Let Me Explain: Impact of Personal and Impersonal Explanations on Trust in Recommender Systems

On the Negative Perception of Cross-domain Recommendations and Explanations

LLM-generated Explanations for Recommender Systems