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Reinterpreting Vulnerability to Tackle Deception in Principles-Based XAI for Human-Computer Interaction

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Explainable and Transparent AI and Multi-Agent Systems (EXTRAAMAS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14127))

Abstract

Artificial intelligence (AI) systems have been increasingly adopted for decision support, behavioral change purposes, assistance, and aid in daily activities and decisions. Thus, focusing on design and interaction that, in addition to being functional, foster users’ acceptance and trust is increasingly necessary. Human-computer interaction (HCI) and human-robot interaction (HRI) studies focused more and more on the exploitation of communication means and interfaces to possibly enact deception. Despite the literal meaning often attributed to the term, deception does not always denote a merely manipulative intent. The expression “banal deception” has been theorized to specifically refer to design strategies that aim to facilitate the interaction. Advances in explainable AI (XAI) could serve as technical means to minimize the risk of distortive effects on people’s perceptions and will. However, this paper argues that how the provided explanations and their content can exacerbate the deceptive dynamics or even manipulate the end user. Therefore, in order to avoid similar consequences, this analysis suggests legal principles to which the explanation must conform to mitigate the side effects of deception in HCI/HRI. Such principles will be made enforceable by assessing the impact of deception on the end users based on the concept of vulnerability – understood here as the rationalization of the inviolable right of human dignity – and control measures implemented in the given systems.

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Acknowledgments

This work is partially supported by the Joint Doctorate grant agreement No 814177 LAST-JD-Rights of Internet of Everything, and the Chist-Era grant CHIST-ERA19-XAI-005, and by (i) the Swiss National Science Foundation (G.A. 20CH21_195530), (ii) the Italian Ministry for Universities and Research, (iii) the Luxembourg National Research Fund (G.A. INTER/CHIST/19/14589586), (iv) the Scientific and Research Council of Turkey (TÜBİTAK, G.A. 120N680).

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Authors and Affiliations

  1. Alma Mater Research Institute for Human-Centered AI, University of Bologna, Bologna, Italy

    Rachele Carli

  2. CLAIM Group and AI RoboLab, University of Luxembourg, Luxembourg, Luxembourg

    Rachele Carli

  3. University of Applied Sciences Western Switzerland, Delémont, Switzerland

    Davide Calvaresi

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  1. Rachele Carli
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Correspondence to Rachele Carli .

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Editors and Affiliations

  1. University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland

    Davide Calvaresi

  2. Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

    Amro Najjar

  3. Alma Mater Studiorum, Università di Bologna, Bologna, Italy

    Andrea Omicini

  4. Ozyegin University, Istanbul, Türkiye

    Reyhan Aydogan

  5. Alma Mater Studiorum, Università di Bologna, Bologna, Italy

    Rachele Carli

  6. Alma Mater Studiorum, Università di Bologna, Bologna, Italy

    Giovanni Ciatto

  7. Université de Technologie de Belfort-Montbéliard, Belfort Cedex, France

    Yazan Mualla

  8. Umeå University, Umeå, Sweden

    Kary Främling

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Carli, R., Calvaresi, D. (2023). Reinterpreting Vulnerability to Tackle Deception in Principles-Based XAI for Human-Computer Interaction. In: Calvaresi, D., et al. Explainable and Transparent AI and Multi-Agent Systems. EXTRAAMAS 2023. Lecture Notes in Computer Science(), vol 14127. Springer, Cham. https://doi.org/10.1007/978-3-031-40878-6_14

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  • DOI: https://doi.org/10.1007/978-3-031-40878-6_14

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