# The Sanction of Authority: Promoting Public Trust in AI

arXiv: https://arxiv.org/abs/2102.04221 · HTML: https://mlsanity.com/paper/2102.04221/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2021.

## Built on by (most cited first, 19 of 19)

- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [Designing for Responsible Trust in AI Systems: A Communication Perspective](https://mlsanity.com/paper/2204.13828/) (2022)
- [Investigating Explainability of Generative AI for Code through Scenario-based Design](https://mlsanity.com/paper/2202.04903/) (2022)
- [Trust in AI: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2403.14680/) (2024)
- [Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI](https://mlsanity.com/paper/2302.00799/) (2023)
- ["It Felt Like Having a Second Mind": Investigating Human-AI Co-creativity in Prewriting with Large Language Models](https://mlsanity.com/paper/2307.10811/) (2023)
- [Humans, AI, and Context: Understanding End-Users' Trust in a Real-World Computer Vision Application](https://mlsanity.com/paper/2305.08598/) (2023)
- [Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations](https://mlsanity.com/paper/2407.08983/) (2024)
- [A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI](https://mlsanity.com/paper/2302.05284/) (2023)
- [Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articles](https://mlsanity.com/paper/2510.21293/) (2025)
- [Certification Labels for Trustworthy AI: Insights From an Empirical Mixed-Method Study](https://mlsanity.com/paper/2305.18307/) (2023)
- [(Un)making AI Magic: a Design Taxonomy](https://mlsanity.com/paper/2403.15216/) (2024)
- [An Empirical Exploration of Trust Dynamics in LLM Supply Chains](https://mlsanity.com/paper/2405.16310/) (2024)
- [Fair Decision-Making for Food Inspections](https://mlsanity.com/paper/2108.05523/) (2021)
- [Pragmatic auditing: a pilot-driven approach for auditing Machine Learning systems](https://mlsanity.com/paper/2405.13191/) (2024)
- [PASTA: A Scalable Framework for Multi-Policy AI Compliance Evaluation](https://mlsanity.com/paper/2601.11702/) (2026)
- [Fiduciary Responsibility: Facilitating Public Trust in Automated Decision Making](https://mlsanity.com/paper/2301.10001/) (2023)
- [Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectives](https://mlsanity.com/paper/2503.13793/) (2025)
- [AI Adoption Across a Multinational Workforce: Sociotechnical Conditions for GenAI Acceptance in Human Resources](https://mlsanity.com/paper/2606.17887/) (2026)

## Abstract (arXiv)

Trusted AI literature to date has focused on the trust needs of users who knowingly interact with discrete AIs. Conspicuously absent from the literature is a rigorous treatment of public trust in AI. We argue that public distrust of AI originates from the under-development of a regulatory ecosystem that would guarantee the trustworthiness of the AIs that pervade society. Drawing from structuration theory and literature on institutional trust, we offer a model of public trust in AI that differs starkly from models driving Trusted AI efforts. This model provides a theoretical scaffolding for Trusted AI research which underscores the need to develop nothing less than a comprehensive and visibly functioning regulatory ecosystem. We elaborate the pivotal role of externally auditable AI documentation within this model and the work to be done to ensure it is effective, and outline a number of actions that would promote public trust in AI. We discuss how existing efforts to develop AI documentation within organizations -- both to inform potential adopters of AI components and support the deliberations of risk and ethics review boards -- is necessary but insufficient assurance of the trustworthiness of AI. We argue that being accountable to the public in ways that earn their trust, through elaborating rules for AI and developing resources for enforcing these rules, is what will ultimately make AI trustworthy enough to be woven into the fabric of our society.