# Reciprocal Trust and Distrust in Artificial Intelligence Systems: The Hard Problem of Regulation

arXiv: https://arxiv.org/abs/2604.05826 · HTML: https://mlsanity.com/paper/2604.05826/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 119 references that are in the map.
- First posted on arXiv 7 Apr 2026.

## Builds on (most cited first, 13 of 13)

- [Alignment faking in large language models](https://mlsanity.com/paper/2412.14093/) (2024)
- [Frontier Models are Capable of In-context Scheming](https://mlsanity.com/paper/2412.04984/) (2024)
- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI](https://mlsanity.com/paper/2010.07487/) (2020)
- [Consciousness in Artificial Intelligence: Insights from the Science of Consciousness](https://mlsanity.com/paper/2308.08708/) (2023)
- [Trustworthy AI](https://mlsanity.com/paper/2011.02272/) (2020)
- [Managing extreme AI risks amid rapid progress](https://mlsanity.com/paper/2310.17688/) (2023)
- [Human-in-the-loop Artificial Intelligence](https://mlsanity.com/paper/1710.08191/) (2017)
- [Trust in AI: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2403.14680/) (2024)
- [The Necessity of AI Audit Standards Boards](https://mlsanity.com/paper/2404.13060/) (2024)
- [To Trust or Distrust Trust Measures: Validating Questionnaires for Trust in AI](https://mlsanity.com/paper/2403.00582/) (2024)
- [Inherent Diverse Redundant Safety Mechanisms for AI-based Software Elements in Automotive Applications](https://mlsanity.com/paper/2402.08208/) (2024)
- [Enhanced Flight Envelope Protection: A Novel Reinforcement Learning Approach](https://mlsanity.com/paper/2406.05586/) (2024)

## Abstract (arXiv)

Policy makers, scientists, and the public are increasingly confronted with thorny questions about the regulation of artificial intelligence (AI) systems. A key common thread concerns whether AI can be trusted and the factors that can make it more trustworthy in front of stakeholders and users. This is indeed crucial, as the trustworthiness of AI systems is fundamental for both democratic governance and for the development and deployment of AI. This article advances the discussion by arguing that AI systems should also be recognized, as least to some extent, as artifacts capable of exercising a form of agency, thereby enabling them to engage in relationships of trust or distrust with humans. It further examines the implications of these reciprocal trust dynamics for regulators tasked with overseeing AI systems. The article concludes by identifying key tensions and unresolved dilemmas that these dynamics pose for the future of AI regulation and governance.