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TExSS: Transparency and Explanations in Smart Systems

Published: 14 April 2021 Publication History

Abstract

Smart systems that apply complex reasoning to make decisions and plan behavior, such as decision support systems and personalized recommendations, are difficult for users to understand. Algorithms allow the exploitation of rich and varied data sources, in order to support human decision-making and/or taking direct actions; however, there are increasing concerns surrounding their transparency and accountability, as these processes are typically opaque to the user. Transparency and accountability have attracted increasing interest to provide more effective system training, better reliability and improved usability. This workshop provides a venue for exploring issues that arise in designing, developing and evaluating intelligent user interfaces that provide system transparency or explanations of their behavior. In addition, we focus on approaches to mitigate algorithmic biases that can be applied by researchers, even without access to a given system’s inter-workings, such as awareness, data provenance, and validation.

References

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Jonathan Dodge, Q. Vera Liao, Yunfeng Zhang, Rachel K.E. Bellamy, and Casey Dugan. 2019. Explaining models: An empirical study of how explanations impact fairness judgment. In International Conference on Intelligent User Interfaces (IUI). https://doi.org/10.1145/3301275.3302310
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Alyssa Glass, Deborah L. McGuinness, and Michael Wolverton. 2008. Toward establishing trust in adaptive agents. In Proceedings of the 13th international conference on Intelligent user interfaces - IUI ’08. 227. https://doi.org/10.1145/1378773.1378804
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Jonathan L. Herlocker, Joseph A. Konstan, and John Riedl. 2000. Explaining collaborative filtering recommendations. In ACM Conference on Computer Supported Cooperative Work (CSCW). 241–250. https://doi.org/10.1145/358916.358995
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Carmen Lacave and Francisco J. Díez. 2002. A review of explanation methods for Bayesian networks., 107–127 pages. https://doi.org/10.1017/S026988890200019X
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Pearl Pu and Li Chen. 2006. Trust building with explanation interfaces. In International conference on Intelligent User Interfaces (IUI). 93. https://doi.org/10.1145/1111449.1111475
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William Swartout, Cecile Paris, and Johanna Moore. 1991. Explanations in knowledge systems: Design for Explainable Expert Systems. IEEE Expert 6, 3 (1991), 58–64. https://doi.org/10.1109/64.87686

Cited By

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  • (2024)Effect of Explanation Conceptualisations on Trust in AI-assisted Credibility AssessmentProceedings of the ACM on Human-Computer Interaction10.1145/36869228:CSCW2(1-31)Online publication date: 8-Nov-2024
  • (2022)Understanding the Role of Explanation Modality in AI-assisted Decision-makingProceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3503252.3531311(223-233)Online publication date: 4-Jul-2022
  • (2021)Building game-playing chatbots using IBM watson assistantProceedings of the 31st Annual International Conference on Computer Science and Software Engineering10.5555/3507788.3507840(282-283)Online publication date: 22-Nov-2021

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    cover image ACM Conferences
    IUI '21 Companion: Companion Proceedings of the 26th International Conference on Intelligent User Interfaces
    April 2021
    101 pages
    ISBN:9781450380188
    DOI:10.1145/3397482
    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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    New York, NY, United States

    Publication History

    Published: 14 April 2021

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    Author Tags

    1. accountability
    2. explanations
    3. fairness
    4. intelligent systems
    5. intelligibility
    6. machine learning
    7. transparency
    8. visualizations

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    Cited By

    View all
    • (2024)Effect of Explanation Conceptualisations on Trust in AI-assisted Credibility AssessmentProceedings of the ACM on Human-Computer Interaction10.1145/36869228:CSCW2(1-31)Online publication date: 8-Nov-2024
    • (2022)Understanding the Role of Explanation Modality in AI-assisted Decision-makingProceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3503252.3531311(223-233)Online publication date: 4-Jul-2022
    • (2021)Building game-playing chatbots using IBM watson assistantProceedings of the 31st Annual International Conference on Computer Science and Software Engineering10.5555/3507788.3507840(282-283)Online publication date: 22-Nov-2021

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