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

2026-03-25 · Source: Journal of Artificial Intelligence Research · Field: Technology & Digital — Artificial Intelligence & Machine Learning, AI Ethics & Responsible AI · Depth: Expert, quick

Summary

A scoping review of the AIES and FAccT conference proceedings reveals that current AI trustworthiness research predominantly adopts techno-centric approaches, focusing on technical attributes like accuracy, reliability, robustness, and fairness. While progress has been made in defining technical aspects such as transparency and accountability, the review identifies critical gaps in addressing the sociotechnical dimensions of AI systems. The analysis highlights that trustworthiness remains a contested concept, often shaped by those in power, and that social and ethical considerations are frequently de-emphasized in favor of technical precision. The study concludes that an interdisciplinary approach is necessary to integrate technical rigor with social, cultural, and institutional factors for a holistic understanding of trustworthy AI.

Key takeaway

For AI Ethicists and Research Scientists developing or evaluating AI systems, recognize that current trustworthiness frameworks are often techno-centric. You should actively integrate social, cultural, and institutional considerations alongside technical attributes to ensure a truly holistic and responsible approach to AI development, promoting equitable outcomes for all stakeholders.

Key insights

AI trustworthiness research overemphasizes technical attributes, neglecting crucial sociotechnical dimensions and ethical considerations.

Principles

• Trustworthy AI requires interdisciplinary integration.
• Trustworthiness is a sociotechnical construct.
• Technical precision often overshadows social ethics.

Method

A scoping review systematically analyzed AIES and FAccT conference proceedings to assess how AI trustworthiness is defined, operationalized, and applied.

In practice

• Adopt holistic AI frameworks.
• Combine technical rigor with social factors.

Topics

• AI Trustworthiness
• Sociotechnical AI Systems
• AIES Conference
• FAccT Conference
• Technical AI Attributes

Best for: AI Scientist, AI Ethicist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Journal of Artificial Intelligence Research.