Keep trusting! A plea for the notion of Trustworthy AI
Bibliographic Data
| ID | 20397155 |
|---|---|
| Authors | Giacomo Zanotti (0000-0001-9898-4113, Politecnico di Milano, corresponding author), Mattia Petrolo (University of Lisbon), Daniele Chiffi (0000-0002-5487-8244, Politecnico di Milano), Viola Schiaffonati (0000-0001-9127-6165, Politecnico di Milano, corresponding author) |
| Year | 2024 |
| Volume | 39 |
| Issue | 6 |
| Pages | 2691-2702 |
| Publication date | 2024-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | AI & Society (JOURNAL) |
| Journal identifiers | ISSN: 0951-5666 • E-ISSN: 1435-5655 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s00146-023-01789-9 |
| OpenAlex | W4387573556 |
| Language | EN |
| Citations received | 13 |
| References cited | 36 |
A lot of attention has recently been devoted to the notion of Trustworthy AI (TAI). However, the very applicability of the notions of trust and trustworthiness to AI systems has been called into question. A purely epistemic account of trust can hardly ground the distinction between trustworthy and merely reliable AI, while it has been argued that insisting on the importance of the trustee’s motivations and goodwill makes the notion of TAI a categorical error. After providing an overview of the debate, we contend that the prevailing views on trust and AI fail to account for the ethically relevant and value-laden aspects of the design and use of AI systems, and we propose an understanding of the notion of TAI that explicitly aims at capturing these aspects. The problems involved in applying trust and trustworthiness to AI systems are overcome by keeping apart trust in AI systems and interpersonal trust. These notions share a conceptual core but should be treated as distinct ones
Business · Epistemology · Express trust · Goodwill · Internet privacy · Interpersonal communication · Plea · Political science · Public relations · Sociology · Trustworthiness · Adversarial Robustness in Machine Learning · Artificial Intelligence in Healthcare and Education · Computer Science · Ethics and Social Impacts of AI · Law · Philosophy · Psychology · Social Psychology
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| Unique citing works | 13 |
|---|---|
| Citations per year | 6,5 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 10 |