research-article

The Conflict Between Explainable and Accountable Decision-Making Algorithms

Authors:

Nina Grgić-Hlača,

Jin Keun Jeong,

Meeyoung ChaAuthors Info & Claims

FAccT '22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency

Pages 2103 - 2113

https://doi.org/10.1145/3531146.3534628

Published: 20 June 2022 Publication History

Abstract

Decision-making algorithms are being used in important decisions, such as who should be enrolled in health care programs and be hired. Even though these systems are currently deployed in high-stakes scenarios, many of them cannot explain their decisions. This limitation has prompted the Explainable Artificial Intelligence (XAI) initiative, which aims to make algorithms explainable to comply with legal requirements, promote trust, and maintain accountability. This paper questions whether and to what extent explainability can help solve the responsibility issues posed by autonomous AI systems. We suggest that XAI systems that provide post-hoc explanations could be seen as blameworthy agents, obscuring the responsibility of developers in the decision-making process. Furthermore, we argue that XAI could result in incorrect attributions of responsibility to vulnerable stakeholders, such as those who are subjected to algorithmic decisions (i.e., patients), due to a misguided perception that they have control over explainable algorithms. This conflict between explainability and accountability can be exacerbated if designers choose to use algorithms and patients as moral and legal scapegoats. We conclude with a set of recommendations for how to approach this tension in the socio-technical process of algorithmic decision-making and a defense of hard regulation to prevent designers from escaping responsibility.

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FAccT '22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency

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DOI:10.1145/3531146

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Abumalloh RNilashi MOoi KTan GChan H(2024)Impact of generative artificial intelligence models on the performance of citizen data scientists in retail firmsComputers in Industry10.1016/j.compind.2024.104128161(104128)Online publication date: Oct-2024
https://doi.org/10.1016/j.compind.2024.104128
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