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TExSS 22: Transparency and Explanations in Smart Systems

Published: 22 March 2022 Publication History

Abstract

Smart systems, such as decision support or recommender systems, continue to prove challenging for people to understand, but are nonetheless ever more pervasive based on the promise of harnessing rich data sources that are becoming available in every domain. These systems tend to be opaque, raising important concerns about how to discover and account for fairness or bias issues. The workshop on Transparency and Explanations in Smart Systems (TExSS) welcomes researchers and practitioners interested in exchanging ideas for overcoming the design, development, and evaluation issues in intelligent user interfaces. Specifically, we will focus on barriers preventing better reliability, trainability, usability, trustworthiness, fairness, accountability, and transparency. This year's theme is “Responsible, Explainable AI for Inclusivity and Trust”, emphasizing the importance of responsibility that tech-industry and developers have towards the design, implementation and evaluation of explainable, inclusive and trustworthy human-AI interaction.

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 the proceedings of the International Conference on Intelligent User Interfaces (IUI). https://doi-org.ezproxy.haifa.ac.il/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.ezproxy.haifa.ac.il/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.ezproxy.haifa.ac.il/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.ezproxy.haifa.ac.il/10.1017/S026988890200019X
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Pearl Pu and Li Chen. 2006. Trust building with explanation interfaces. In the proceedings of the International conference on Intelligent User Interfaces (IUI). 93. https://doi-org.ezproxy.haifa.ac.il/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.ezproxy.haifa.ac.il/10.1109/64.87686

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    IUI '22 Companion: Companion Proceedings of the 27th International Conference on Intelligent User Interfaces
    March 2022
    142 pages
    ISBN:9781450391450
    DOI:10.1145/3490100
    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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    Published: 22 March 2022

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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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