# Autonomy by Design: Preserving Human Autonomy in AI Decision-Support

arXiv: https://arxiv.org/abs/2506.23952 · HTML: https://mlsanity.com/paper/2506.23952/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 93 references that are in the map.
- First posted on arXiv 30 Jun 2025.

## Builds on (most cited first, 23 of 23)

- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Natural Adversarial Examples](https://mlsanity.com/paper/1907.07174/) (2019)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Manipulating and Measuring Model Interpretability](https://mlsanity.com/paper/1802.07810/) (2018)
- [Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study](https://mlsanity.com/paper/2303.17466/) (2023)
- [Co-Writing with Opinionated Language Models Affects Users' Views](https://mlsanity.com/paper/2302.00560/) (2023)
- [Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects](https://mlsanity.com/paper/1811.11553/) (2018)
- [Abstracting Causal Models](https://mlsanity.com/paper/1812.03789/) (2018)
- [The Ghost in the Machine has an American accent: value conflict in GPT-3](https://mlsanity.com/paper/2203.07785/) (2022)
- [Multivariate Confidence Calibration for Object Detection](https://mlsanity.com/paper/2004.13546/) (2020)
- [Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations](https://mlsanity.com/paper/2302.02187/) (2023)
- [Understanding the Benefits and Challenges of Using Large Language Model-based Conversational Agents for Mental Well-being Support](https://mlsanity.com/paper/2307.15810/) (2023)
- [Knowing About Knowing: An Illusion of Human Competence Can Hinder Appropriate Reliance on AI Systems](https://mlsanity.com/paper/2301.11333/) (2023)
- [Assessing LLMs for Moral Value Pluralism](https://mlsanity.com/paper/2312.10075/) (2023)
- [User Tampering in Reinforcement Learning Recommender Systems](https://mlsanity.com/paper/2109.04083/) (2021)
- [A Human Rights-Based Approach to Responsible AI](https://mlsanity.com/paper/2210.02667/) (2022)
- [Exploring a Behavioral Model of "Positive Friction" in Human-AI Interaction](https://mlsanity.com/paper/2402.09683/) (2024)
- [AI, Opacity, and Personal Autonomy](https://mlsanity.com/paper/2210.08995/) (2022)
- [Legitimacy, Authority, and Democratic Duties of Explanation](https://mlsanity.com/paper/2208.08628/) (2022)
- [Study on the Helpfulness of Explainable Artificial Intelligence](https://mlsanity.com/paper/2410.11896/) (2024)
- [RAIL in the Wild: Operationalizing Responsible AI Evaluation Using Anthropic's Value Dataset](https://mlsanity.com/paper/2505.00204/) (2025)
- [A Value-Centered Exploration of Data Privacy and Personalized Privacy Assistants](https://mlsanity.com/paper/2212.00528/) (2022)
- [An AI-Powered Research Assistant in the Lab: A Practical Guide for Text Analysis Through Iterative Collaboration with LLMs](https://mlsanity.com/paper/2505.09724/) (2025)

## Built on by (most cited first, 2 of 2)

- [Beyond Compliance: How AI Could Help Creative Writers by Refusing Them](https://mlsanity.com/paper/2605.16272/) (2026)
- [Reframing AI Loss of Control: What Control Is, How to Have It, How to Lose It](https://mlsanity.com/paper/2606.12442/) (2026)

## Abstract (arXiv)

AI systems increasingly support human decision-making across domains of professional, skill-based, and personal activity. While previous work has examined how AI might affect human autonomy globally, the effects of AI on domain-specific autonomy -- the capacity for self-governed action within defined realms of skill or expertise -- remain understudied. We analyze how AI decision-support systems affect two key components of domain-specific autonomy: skilled competence (the ability to make informed judgments within one's domain) and authentic value-formation (the capacity to form genuine domain-relevant values and preferences). By engaging with prior investigations and analyzing empirical cases across medical, financial, and educational domains, we demonstrate how the absence of reliable failure indicators and the potential for unconscious value shifts can erode domain-specific autonomy both immediately and over time. We then develop a constructive framework for autonomy-preserving AI support systems. We propose specific socio-technical design patterns -- including careful role specification, implementation of defeater mechanisms, and support for reflective practice -- that can help maintain domain-specific autonomy while leveraging AI capabilities. This framework provides concrete guidance for developing AI systems that enhance rather than diminish human agency within specialized domains of action.