# AI, Opacity, and Personal Autonomy

arXiv: https://arxiv.org/abs/2210.08995 · HTML: https://mlsanity.com/paper/2210.08995/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 94 references that are in the map.
- First posted on arXiv 25 Sep 2022.

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

- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Actionable Recourse in Linear Classification](https://mlsanity.com/paper/1809.06514/) (2018)
- [Scalable and accurate deep learning for electronic health records](https://mlsanity.com/paper/1801.07860/) (2018)
- [What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research](https://mlsanity.com/paper/2102.07817/) (2021)
- [Principles and Practice of Explainable Machine Learning](https://mlsanity.com/paper/2009.11698/) (2020)
- [Measuring the Quality of Explanations: The System Causability Scale (SCS). Comparing Human and Machine Explanations](https://mlsanity.com/paper/1912.09024/) (2019)
- [The Pragmatic Turn in Explainable Artificial Intelligence (XAI)](https://mlsanity.com/paper/2002.09595/) (2020)
- [Taking Principles Seriously: A Hybrid Approach to Value Alignment](https://mlsanity.com/paper/2012.11705/) (2020)
- [Governance by Glass-Box: Implementing Transparent Moral Bounds for AI Behaviour](https://mlsanity.com/paper/1905.04994/) (2019)
- [Natural Language Generation enhances human decision-making with uncertain information](https://mlsanity.com/paper/1606.03254/) (2016)
- [Zombies in the Loop? Humans Trust Untrustworthy AI-Advisors for Ethical Decisions](https://mlsanity.com/paper/2106.16122/) (2021)

## Built on by (most cited first, 5 of 5)

- [AI Alignment through Reinforcement Learning from Human Feedback? Contradictions and Limitations](https://mlsanity.com/paper/2406.18346/) (2024)
- [Autonomy by Design: Preserving Human Autonomy in AI Decision-Support](https://mlsanity.com/paper/2506.23952/) (2025)
- [Automating privacy decisions -- where to draw the line?](https://mlsanity.com/paper/2305.08747/) (2023)
- [Examining Users' Behavioural Intention to Use OpenClaw Through the Cognition--Affect--Conation Framework](https://mlsanity.com/paper/2603.11455/) (2026)
- [Becoming Suspicious Across Borders: Algorithmic Extraterritoriality and AI-Driven Financial Surveillance](https://mlsanity.com/paper/2610.03425/) (2026)

## Abstract (arXiv)

Advancements in machine learning have fuelled the popularity of using AI decision algorithms in procedures such as bail hearings (Feller et al. 2016), medical diagnoses (Rajkomar et al. 2018; Esteva et al. 2019) and recruitment (Heilweil 2019, Van Esch et al. 2019). Academic articles (Floridi et al. 2018), policy texts (HLEG 2019), and popularizing books (O'Neill 2016, Eubanks 2018) alike warn that such algorithms tend to be _opaque_: they do not provide explanations for their outcomes. Building on a causal account of transparency and opacity as well as recent work on the value of causal explanation (Lombrozo 2011, Hitchcock 2012), I formulate a moral concern for opaque algorithms that is yet to receive a systematic treatment in the literature: when such algorithms are used in life-changing decisions, they can obstruct us from effectively shaping our lives according to our goals and preferences, thus undermining our autonomy. I argue that this concern deserves closer attention as it furnishes the call for transparency in algorithmic decision-making with both new tools and new challenges.