Trustworthy artificial intelligence

By Mona Simion, Christoph Kelp. 2023

This paper develops an account of trustworthy AI. Its central idea is that whether AIs are trustworthy is a matter of whether they live up to their function-based obligations. We argue that this account serves to advance the literature in a couple of important ways. First, it serves to provide a rationale for why a range of properties that are widely assumed in the scientific literature, as well as in policy, to be required of trustworthy AI, such as safety, justice, and explainability, are properties (often) instantiated by trustworthy AI. Second, we connect the discussion on trustworthy AI in policy, industry, and the sciences with the philosophical discussion of trustworthiness. We argue that extant accounts of trustworthiness in the philosophy literature cannot make proper sense of trustworthy AI and that our account compares favourably with its competitors on this front.

Subfields: Safety, Robustness & Interpretability, AI Ethics, Fairness & Society

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Citations in the corpus

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “It is universally agreed that interpretability is a key element of trust for AI models (Wagstaff, 2012; Rudin and Wagstaff, 2014; Lo Piano, 2020; Ashoori and Weisz, 2019; Thiebes et al., 2020; Spiegelhalter, 2020; Brundage et al., 2020).”
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI (2020, background): “This perspective distinguishes “trust” (an attitude of the trustor) from being “trustworthy” (a property of the trustee) [68, 75, 80], and we say that anAImodel is trustworthy to some contract if it is capable of maintaining this contract.”