CoChat
Sign Up
Cited by 47Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)Cybernetics and Technology in Society

Trust and Trustworthiness in AI

Open access
Durán, Juan Manuel, Pozzi, Giorgia·2025·Philosophy & Technology
DOI 10.1007/s13347-025-00843-2
Open this paper in CoChat — free accountOpen PDF

Abstract

Achieving trustworthy AI is increasingly considered an essential desideratum to integrate AI systems into sensitive societal fields, such as criminal justice, finance, medicine, and healthcare, among others. For this reason, it is important to spell out clearly its characteristics, merits, and shortcomings. This article is the first survey in the specialized literature that maps out the philosophical landscape surrounding trust and trustworthiness in AI. To achieve our goals, we proceed as follows. We start by discussing philosophical positions on trust and trustworthiness, focusing on interpersonal accounts of trust. This allows us to explain why trust, in its most general terms, is to be understood as reliance plus some “extra factor”. We then turn to the first part of the definition provided, i.e., reliance, and analyze two opposing approaches to establishing AI systems’ reliability. On the one hand, we consider transparency and, on the other, computational reliabilism . Subsequently, we focus on debates revolving around the “extra factor”. To this end, we consider viewpoints that most actively resist the possibility and desirability of trusting AI systems before turning to the analysis of the most prominent advocates of it. Finally, we take up the main conclusions of the previous sections and briefly point at issues that remain open and need further attention.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s13347-025-00843-2

Cites

54 of 54 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Trust is an overarching theme that arises in all previously discussed topics and the development and deployment of AI generally.

    “Trust is an overarching theme that arises in all previously discussed topics and the development and deployment of AI generally [5, 138, 139, 145, 146].”

    From Autonomous artificial intelligence, scientific research, and human values (2026) · cites this paper as [146]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

3 of the 57 references couldn’t be matched to a known paper.

Research works printed but not linked: 5

  1. Doshi-Velez, F., & Kim ,B. (2017). Towards a Rigorous Science of interpretable machine learning. https:// arxiv. org/ abs/ 1702. 08608. Accessed 27 Jan 2025.
  2. Durán, J. M. forthcoming. Beyond transparency: computational reliabilism as an externalist epistemology for algorithms. In J. M. Durán, & G. Pozzi (Eds.), Philosophy of Science for Machine Learning: Core Issues and New Perspectives. Synthese Library: Springer.
  3. Elgin, C. Z. (1996). Considered judgement. Princeton University Press.
  4. McLeod, C. (2021). Trust. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy (Fall 2021 Edi- tion). https:// plato. stanf ord. edu/ archi ves/ fall2 021/ entri es/ trust/. Accessed 27 Jan 2025.
  5. Wu, X., & Zhang X. (2017). Responses to critiques on machine learning of criminality perceptions (Addendum of arXiv:1611.04135). arXiv. https:// doi. org/ 10. 48550/ arXiv. 1611. 04135 · DOI

Cited by

Sorted by citations

Related

Authors
Durán, J. M., & Pozzi, G.
Affiliations
Delft University of Technology (NL)
Corresponding author
Durán, Juan Manuel
ORCID
Venue
Philosophy & Technology
Publisher
Springer Science and Business Media LLC
Published
2025-02-04
Volume
38
Issue
1
ISSN
  • 2210-5433 (ISSN-L)
  • 2210-5441
DOI
10.1007/s13347-025-00843-2
OpenAlex
W4407094556
Cited by
47
References
57
Citations by year
2026: 31 · 2025: 16
FWCI
48.44
Type
journal article
Language
en
Open access
Licence
Locations
Philosophy & Technology (publishedVersion, cc-by)
Funders
Horizon 2020 Framework Programme (871042)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Social Sciences › Arts and Humanities › History and Philosophy of Science › Cybernetics and Technology in Society
Keywords
trustworthy AI, trust in AI, trustworthiness, reliance, transparency, AI reliability, philosophy of trust

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review