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arxiv: 2510.21293 · v2 · submitted 2025-10-24 · 💻 cs.AI · cs.HC

Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articles

Siddharth Mehrotra , Jin Huang , Xuelong Fu , Roel Dobbe , Clara I. S\'anchez , Maarten de Rijke This is my paper

Pith reviewed 2026-05-18 04:54 UTC · model grok-4.3

classification 💻 cs.AI cs.HC
keywords AI trustworthinesssociotechnical systemsscoping reviewAI ethicsconceptualizationmeasurementvalidation
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The pith

Current AI trustworthiness research emphasizes technical precision while underplaying social and ethical factors.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper is a scoping review investigating how AI trustworthiness is conceptualized, measured, and validated within the AI ethics community. It argues that existing work focuses heavily on technical attributes like reliability and fairness but neglects the sociotechnical aspects essential for real-world impact. If accurate, this indicates that trustworthiness remains a contested idea influenced by power structures rather than a balanced framework. A sympathetic reader would care because it highlights the need for better integration of social and ethical factors to ensure AI benefits society broadly. The conclusion calls for interdisciplinary methods to address these shortcomings.

Core claim

While significant progress has been made in defining technical attributes such as transparency, accountability, and robustness, current research often predominantly emphasizes technical precision at the expense of social and ethical considerations. The sociotechnical nature of AI systems remains less explored and trustworthiness emerges as a contested concept shaped by those with the power to define it.

What carries the argument

The scoping review that systematically analyzes conceptualization approaches, measurement methods, verification and validation techniques, application areas, and underlying values across the examined literature.

If this is right

  • Interdisciplinary approaches are needed to combine technical rigor with social, cultural, and institutional considerations.
  • Holistic frameworks should address the complex interplay between AI systems and society.
  • Responsible technological development should benefit all stakeholders through broader inclusion of ethical dimensions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Developers could incorporate power analysis into trustworthiness assessments to avoid definitions dominated by narrow interests.
  • Extending similar reviews beyond the sampled literature might confirm whether the technical emphasis is widespread.
  • Policy guidelines for AI deployment could mandate explicit evaluation of sociotechnical impacts.

Load-bearing premise

The selection of articles and qualitative coding accurately captures dominant patterns and gaps in the AI ethics literature without significant bias.

What would settle it

A comprehensive study showing balanced integration of technical and sociotechnical factors in defining and validating AI trustworthiness across multiple domains.

Figures

Figures reproduced from arXiv: 2510.21293 by Clara I. S\'anchez, Jin Huang, Maarten de Rijke, Roel Dobbe, Siddharth Mehrotra, Xuelong Fu.

Figure 1
Figure 1. Figure 1: Flowchart of the articles reviewing process following the PRISMA protocol ( [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Heatmap of the normalized frequencies of trustworthiness dimensions in each paper of the final corpus ( [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Visualizations of topics generated using BERTopic applied to the initial keyword-based selection of articles ( [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
read the original abstract

Background: Trustworthy AI serves as a foundational pillar for two major AI ethics conferences: AIES and FAccT. However, current research often adopts techno-centric approaches, focusing primarily on technical attributes such as reliability, robustness, and fairness, while overlooking the sociotechnical dimensions critical to understanding AI trustworthiness in real-world contexts. Objectives: This scoping review aims to examine how the AIES and FAccT communities conceptualize, measure, and validate AI trustworthiness, identifying major gaps and opportunities for advancing a holistic understanding of trustworthy AI systems. Methods: We conduct a scoping review of AIES and FAccT conference proceedings to date, systematically analyzing how trustworthiness is defined, operationalized, and applied across different research domains. Our analysis focuses on conceptualization approaches, measurement methods, verification and validation techniques, application areas, and underlying values. Results: While significant progress has been made in defining technical attributes such as transparency, accountability, and robustness, our findings reveal critical gaps. Current research often predominantly emphasizes technical precision at the expense of social and ethical considerations. The sociotechnical nature of AI systems remains less explored and trustworthiness emerges as a contested concept shaped by those with the power to define it. Conclusions: An interdisciplinary approach combining technical rigor with social, cultural, and institutional considerations is essential for advancing trustworthy AI. We propose actionable measures for the AI ethics community to adopt holistic frameworks that genuinely address the complex interplay between AI systems and society, ultimately promoting responsible technological development that benefits all stakeholders.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. This paper conducts a scoping review of AIES and FAccT conference proceedings to analyze how AI trustworthiness is conceptualized, measured, and validated. It reports that research predominantly emphasizes technical attributes such as reliability, robustness, transparency, and fairness while under-exploring sociotechnical dimensions, rendering trustworthiness a contested concept shaped by definitional power. The authors conclude that an interdisciplinary approach integrating technical, social, cultural, and institutional factors is needed and propose actionable measures for the community.

Significance. If the synthesis holds, the review usefully maps progress and gaps across two central AI ethics venues, crediting technical advances in areas like accountability and robustness while identifying the need for more holistic frameworks. This targeted scoping of conference literature could help orient future work toward genuine sociotechnical integration.

major comments (1)
  1. [Methods] Methods section: the description of systematic analysis and thematic synthesis provides no details on the coding framework, number of independent coders, inter-rater reliability metrics, or procedures for resolving borderline cases (e.g., papers addressing both technical transparency and stakeholder power). This is load-bearing for the central claims that technical precision predominates and that sociotechnical aspects remain less explored, because those gap findings rest entirely on the reliability and consistency of the qualitative coding.
minor comments (1)
  1. [Abstract] Abstract, Results paragraph: the summary of gaps would be strengthened by citing one or two concrete examples of reviewed papers that illustrate the technical emphasis versus sociotechnical omission.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our scoping review. We agree that greater methodological transparency is needed to support the reliability of the thematic findings and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Methods] Methods section: the description of systematic analysis and thematic synthesis provides no details on the coding framework, number of independent coders, inter-rater reliability metrics, or procedures for resolving borderline cases (e.g., papers addressing both technical transparency and stakeholder power). This is load-bearing for the central claims that technical precision predominates and that sociotechnical aspects remain less explored, because those gap findings rest entirely on the reliability and consistency of the qualitative coding.

    Authors: We agree that the current Methods section is insufficiently detailed on the qualitative analysis procedures. In the revised manuscript we will expand this section to describe the coding framework (developed iteratively from the review objectives and prior AI ethics literature), the involvement of two independent coders with a third author adjudicating disagreements, the calculation of inter-rater reliability (Cohen’s kappa), and explicit procedures for resolving borderline cases such as papers that combine technical transparency with stakeholder power considerations. These additions will directly address the concern that the gap findings rest on unverified coding consistency. revision: yes

Circularity Check

0 steps flagged

No significant circularity in scoping review synthesis

full rationale

The paper performs a scoping review of external AIES and FAccT conference proceedings, systematically coding conceptualization, measurement, and validation approaches from those sources. Central claims about emphasis on technical attributes versus sociotechnical gaps derive directly from this analysis of independent literature rather than self-definitions, fitted parameters, or load-bearing self-citations. No equations, derivations, or uniqueness theorems reduce the findings to the authors' inputs by construction. The synthesis remains self-contained against the reviewed external papers.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The review rests on the domain assumption that trustworthiness must integrate technical and sociotechnical dimensions and that conference proceedings are a representative sample of the field; no free parameters or new entities are introduced.

axioms (1)
  • domain assumption AI trustworthiness research benefits from balancing technical attributes with social, cultural, and institutional considerations.
    This premise drives the identification of gaps and the call for interdisciplinary approaches in the conclusions.

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Reference graph

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