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Risk and Exposure of XAI in Persuasion and Argumentation: The case of Manipulation

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Explainable and Transparent AI and Multi-Agent Systems (EXTRAAMAS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13283))

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Abstract

In the last decades, Artificial intelligence (AI) systems have been increasingly adopted in assistive (possibly collaborative) decision-making tools. In particular, AI-based persuasive technologies are designed to steer/influence users’ behaviour, habits, and choices to facilitate the achievement of their own - predetermined - goals. Nowadays, the inputs received by the assistive systems leverage heavily AI data-driven approaches. Thus, it is imperative to have transparent and understandable (to the user) both the process leading to the recommendations and the recommendations. The Explainable AI (XAI) community has progressively contributed to “opening the black box”, ensuring the interaction’s effectiveness, and pursuing the safety of the individuals involved. However, principles and methods ensuring the efficacy and information retain on the human have not been introduced yet. The risk is to underestimate the context dependency and subjectivity of the explanations’ understanding, interpretation, and relevance. Moreover, even a plausible (and possibly expected) explanation can lead to an imprecise or incorrect outcome or its understanding. This can lead to unbalanced and unfair circumstances, such as giving a financial advantage to the system owner/provider and the detriment of the user.

This paper highlights that the sole explanations - especially in the context of persuasive technologies - are not self-sufficient to protect users’ psychological and physical integrity. Conversely, explanations could be misused, becoming themselves a tool of manipulation. Therefore, we suggest characteristics safeguarding the explanation from being manipulative and legal principles to be used as criteria for evaluating the operation of XAI systems, both from an ex-ante and ex-post perspective.

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Notes

  1. 1.

    https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206.

  2. 2.

    https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206.

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Acknowledgments

This work has received funding from the Joint Doctorate grant agreement No 814177 LAST-JD-Rights of Internet of Everything.

This work is partially supported by 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. University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Rachele Carli

  3. Luxembourg Institute of Science and Technology (LIST), Esch-sur-Alzette, Luxembourg

    Amro Najjar

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

    Davide Calvaresi

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  1. Rachele Carli
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Editors and Affiliations

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

    Davide Calvaresi

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

    Amro Najjar

  3. Victoria University of Wellington, Wellington, New Zealand

    Michael Winikoff

  4. Umeå University, Umeå, Sweden

    Kary Främling

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Carli, R., Najjar, A., Calvaresi, D. (2022). Risk and Exposure of XAI in Persuasion and Argumentation: The case of Manipulation. In: Calvaresi, D., Najjar, A., Winikoff, M., Främling, K. (eds) Explainable and Transparent AI and Multi-Agent Systems. EXTRAAMAS 2022. Lecture Notes in Computer Science(), vol 13283. Springer, Cham. https://doi.org/10.1007/978-3-031-15565-9_13

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