# Guilty Artificial Minds

arXiv: https://arxiv.org/abs/2102.04209 · HTML: https://mlsanity.com/paper/2102.04209/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 114 references that are in the map.
- First posted on arXiv 24 Jan 2021.

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

- [Machine Theory of Mind](https://mlsanity.com/paper/1802.07740/) (2018)
- [Implementations in Machine Ethics: A Survey](https://mlsanity.com/paper/2001.07573/) (2020)

## Built on by (most cited first, 9 of 9)

- [Large Language Models: The Need for Nuance in Current Debates and a Pragmatic Perspective on Understanding](https://mlsanity.com/paper/2310.19671/) (2023)
- [From "AI" to Probabilistic Automation: How Does Anthropomorphization of Technical Systems Descriptions Influence Trust?](https://mlsanity.com/paper/2404.16047/) (2024)
- [Perceptions of Sentient AI and Other Digital Minds: Evidence from the AI, Morality, and Sentience (AIMS) Survey](https://mlsanity.com/paper/2407.08867/) (2024)
- [The AI Double Standard: Humans Judge All AIs for the Actions of One](https://mlsanity.com/paper/2412.06040/) (2024)
- [Unravelling Responsibility for AI](https://mlsanity.com/paper/2308.02608/) (2023)
- [Mental Models of Autonomy and Sentience Shape Reactions to AI](https://mlsanity.com/paper/2512.09085/) (2025)
- [Mindful Explanations: Prevalence and Impact of Mind Attribution in XAI Research](https://mlsanity.com/paper/2312.12119/) (2023)
- [Human Attribution of Causality to AI Across Agency, Misuse, and Misalignment](https://mlsanity.com/paper/2603.13236/) (2026)
- [Method, Mind, and Morality: How People Make Sense of Artificial Intelligence](https://mlsanity.com/paper/2608.24748/) (2026)

## Abstract (arXiv)

The concepts of blameworthiness and wrongness are of fundamental importance in human moral life. But to what extent are humans disposed to blame artificially intelligent agents, and to what extent will they judge their actions to be morally wrong? To make progress on these questions, we adopted two novel strategies. First, we break down attributions of blame and wrongness into more basic judgments about the epistemic and conative state of the agent, and the consequences of the agent's actions. In this way, we are able to examine any differences between the way participants treat artificial agents in terms of differences in these more basic judgments. our second strategy is to compare attributions of blame and wrongness across human, artificial, and group agents (corporations). Others have compared attributions of blame and wrongness between human and artificial agents, but the addition of group agents is significant because these agents seem to provide a clear middle-ground between human agents (for whom the notions of blame and wrongness were created) and artificial agents (for whom the question remains open).