# Moral Responsibility for AI Systems

arXiv: https://arxiv.org/abs/2310.18040 · HTML: https://mlsanity.com/paper/2310.18040/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 29 references that are in the map.
- First posted on arXiv 27 Oct 2023.

## Builds on (most cited first, 2 of 2)

- [Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning](https://mlsanity.com/paper/2202.05338/) (2022)
- [Causal Sufficiency and Actual Causation](https://mlsanity.com/paper/2102.02311/) (2021)

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

- [AI Awareness](https://mlsanity.com/paper/2504.20084/) (2025)
- [Why Sampling Is Not Choosing: Intentionality, Agency, and Moral Responsibility in Large Language Models](https://mlsanity.com/paper/2606.13441/) (2026)

## Abstract (arXiv)

As more and more decisions that have a significant ethical dimension are being outsourced to AI systems, it is important to have a definition of moral responsibility that can be applied to AI systems. Moral responsibility for an outcome of an agent who performs some action is commonly taken to involve both a causal condition and an epistemic condition: the action should cause the outcome, and the agent should have been aware -- in some form or other -- of the possible moral consequences of their action. This paper presents a formal definition of both conditions within the framework of causal models. I compare my approach to the existing approaches of Braham and van Hees (BvH) and of Halpern and Kleiman-Weiner (HK). I then generalize my definition into a degree of responsibility.