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Some critical and ethical perspectives on the empirical turn of AI interpretability

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  • John-Mathews, Jean-Marie

Abstract

We consider two fundamental and related issues currently facing the development of Artificial Intelligence (AI): the lack of ethics, and the interpretability of AI decisions. Can interpretable AI decisions help to address the issue of ethics in AI? Using a randomized study, we experimentally show that the empirical and liberal turn of the production of explanations tends to select AI explanations with a low denunciatory power. Under certain conditions, interpretability tools are therefore not means but, paradoxically, obstacles to the production of ethical AI since they can give the illusion of being sensitive to ethical incidents. We also show that the denunciatory power of AI explanations is highly dependent on the context in which the explanation takes place, such as the gender or education of the person for whom the explication is intended. AI ethics tools are therefore sometimes too flexible and self-regulation through the liberal production of explanations does not seem to be enough to resolve ethical issues. By following an STS pragmatist program, we highlight the role of non-human actors (such as computational paradigms, testing environments, etc.) in the formation of structural power relations, such as sexism. We then propose two scenarios for the future development of ethical AI: more external regulation, or more liberalization of AI explanations. These two opposite paths will play a major role in the future development of ethical AI.

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  • John-Mathews, Jean-Marie, 2022. "Some critical and ethical perspectives on the empirical turn of AI interpretability," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    • Handle: RePEc:eee:tefoso:v:174:y:2022:i:c:s0040162521006429
    DOI: 10.1016/j.techfore.2021.121209
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    References listed on IDEAS

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    1. Arun Rai, 2020. "Explainable AI: from black box to glass box," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 137-141, January.
    2. Wang, Huamao & Yao, Yumei & Salhi, Said, 2020. "Tension in big data using machine learning: Analysis and applications," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    3. Wang, Yichuan & Kung, LeeAnn & Byrd, Terry Anthony, 2018. "Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 3-13.
    4. Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2018. "Human Decisions and Machine Predictions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 237-293.
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    Cited by:

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    2. Suen, Hung-Yue & Hung, Kuo-En, 2024. "Revealing the influence of AI and its interfaces on job candidates' honest and deceptive impression management in asynchronous video interviews," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    3. Radu Valentin & Croitoru Ionut Marius & Tabirca Alina Iuliana & Stoica Silviu-Ionel, 2023. "Ai Components For Performance Measurement - A Bibliometric Approach," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 6, pages 286-300, December.
    4. Chen, Xun-Qi & Ma, Chao-Qun & Ren, Yi-Shuai & Lei, Yu-Tian & Huynh, Ngoc Quang Anh & Narayan, Seema, 2023. "Explainable artificial intelligence in finance: A bibliometric review," Finance Research Letters, Elsevier, vol. 56(C).
    5. Herrera, Gabriel Paes & Constantino, Michel & Su, Jen-Je & Naranpanawa, Athula, 2023. "The use of ICTs and income distribution in Brazil: A machine learning explanation using SHAP values," Telecommunications Policy, Elsevier, vol. 47(8).
    6. Behera, Rajat Kumar & Bala, Pradip Kumar & Rana, Nripendra P. & Irani, Zahir, 2023. "Responsible natural language processing: A principlist framework for social benefits," Technological Forecasting and Social Change, Elsevier, vol. 188(C).

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