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Creating a User Model to Support User-specific Explanations of AI Systems

Authors:

Owen Chambers,

Robin Cohen,

Maura R. Grossman,

Queenie ChenAuthors Info & Claims

UMAP '22 Adjunct: Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization

Pages 163 - 166

https://doi.org/10.1145/3511047.3537678

Published: 04 July 2022 Publication History

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Abstract

In this paper, we present a framework that supports providing user-specific explanations of AI systems. This is achieved by proposing a particular approach for modeling a user which enables a decision procedure to reason about how much detail to provide in an explanation. We also clarify the circumstances under which it is best not to provide an explanation at all, as one novel aspect of our design. While transparency of black box AI systems is an important aim for ethical AI, efforts to date are often one-size-fits-all. Our position is that more attention should be paid towards offering explanations that are context-specific, and our model takes an important step forward towards achieving that aim.

References

[1]

Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador Garcia, Sergio Gil-Lopez, Daniel Molina, Richard Benjamins, Raja Chatila, and Francisco Herrera. 2020. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion 58(2020), 82–115. https://doi.org/10.1016/j.inffus.2019.12.012

Digital Library

Google Scholar

[2]

John A Bateman and Cecile Paris. 1989. Phrasing a text in terms the user can understand. In IJCAI. 1511–1517.

Google Scholar

[3]

Maartje MA De Graaf and Somaya Ben Allouch. 2013. Exploring influencing variables for the acceptance of social robots. Robotics and autonomous systems 61, 12 (2013), 1476–1486.

Google Scholar

[4]

Gerhard Fischer. 2000. User Modeling in Human-Computer Interaction. User Modeling and User-Adapted Interaction 11 (08 2000). https://doi.org/10.1023/A:1011145532042

Crossref

Google Scholar

[5]

Kevin Anthony Hoff and Masooda Bashir. 2015. Trust in automation: Integrating empirical evidence on factors that influence trust. Human factors 57, 3 (2015), 407–434.

Crossref

Google Scholar

[6]

Clodéric Mars, Rémi Dès, and Matthieu Boussard. 2020. The three stages of Explainable AI: How explainability facilitates real world deployment of AI.

Google Scholar

[7]

Gerald Matthews, Jinchao Lin, April Rose Panganiban, and Michael D Long. 2019. Individual differences in trust in autonomous robots: Implications for transparency. IEEE Transactions on Human-Machine Systems 50, 3 (2019), 234–244.

Crossref

Google Scholar

[8]

Stephanie M Merritt and Daniel R Ilgen. 2008. Not all trust is created equal: Dispositional and history-based trust in human-automation interactions. Human factors 50, 2 (2008), 194–210.

Google Scholar

[9]

Tatsuya Nomura, Takayuki Kanda, Tomohiro Suzuki, and Kensuke Kato. 2008. Prediction of Human Behavior in Human–Robot Interaction Using Psychological Scales for Anxiety and Negative Attitudes Toward Robots. IEEE Transactions on Robotics 24, 2 (2008), 442–451. https://doi.org/10.1109/TRO.2007.914004

Digital Library

Google Scholar

[10]

Anders Persson, Mikael Laaksoharju, and Hiroshi Koga. 2021. We Mostly Think Alike: Individual Differences in Attitude Towards AI in Sweden and Japan. The Review of Socionetwork Strategies 15, 1 (01 May 2021), 123–142. https://doi.org/10.1007/s12626-021-00071-y

Crossref

Google Scholar

[11]

Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. ”Why Should I Trust You?”: Explaining the Predictions of Any Classifier. CoRR abs/1602.04938(2016). arXiv:1602.04938

Google Scholar

[12]

James L. Szalma and Grant S. Taylor. 2011. Individual differences in response to automation: the five factor model of personality.Journal of experimental psychology. Applied 17 2 (2011), 71–96.

Google Scholar

[13]

Xinru Wang and Ming Yin. 2021. Are Explanations Helpful? A Comparative Study of the Effects of Explanations in AI-Assisted Decision-Making. Association for Computing Machinery, New York, NY, USA, 318–328.

Digital Library

Google Scholar

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Jeromela JConlan OBoratto LGena CMarras MGermanakos PPopescus E(2024)Devising Scrutable User Models for Time Management AssistantsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665182(250-255)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3665182
Geninatti Cossatin ABoratto LGena CMarras MGermanakos PPopescus E(2024)Promoting Green Fashion Consumption in Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664922(50-54)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664922
Bach TKristiansen JBabic AJacovi A(2024)Unpacking Human-AI Interaction in Safety-Critical Industries: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2024.343719012(106385-106414)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3437190

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Information

Published In

UMAP '22 Adjunct: Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization

July 2022

409 pages

ISBN:9781450392327

DOI:10.1145/3511047

General Chairs:
Alejandro Bellogin
Universidad Autonoma de Madrid, Spain
,
Ludovico Boratto
University of Cagliari, Italy
,
Olga C. Santos
UNED, Spain
,
Program Chairs:
Liliana Ardissono
Universita di Torino, Italy
,
Bart Knijnenburg
Clemson University, United States

Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Published: 04 July 2022

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UMAP '22: 30th ACM Conference on User Modeling, Adaptation and Personalization

July 4 - 7, 2022

Barcelona, Spain

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Overall Acceptance Rate 162 of 633 submissions, 26%

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33rd ACM Conference on User Modeling, Adaptation and Personalization

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Jeromela JConlan OBoratto LGena CMarras MGermanakos PPopescus E(2024)Devising Scrutable User Models for Time Management AssistantsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665182(250-255)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3665182
Geninatti Cossatin ABoratto LGena CMarras MGermanakos PPopescus E(2024)Promoting Green Fashion Consumption in Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664922(50-54)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664922
Bach TKristiansen JBabic AJacovi A(2024)Unpacking Human-AI Interaction in Safety-Critical Industries: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2024.343719012(106385-106414)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3437190
Hu ZMauro NPetrone GArdissono L(2023)Service-based Presentation of Multimodal Information for the Justification of Recommender Systems ResultsProceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3565472.3592962(46-53)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3565472.3592962

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Complexity results for explanations in the structural-model approach

Categorical and Continuous Features in Counterfactual Explanations of AI Systems

The Use and Misuse of Counterfactuals in Ethical Machine Learning

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