# Blaming Humans and Machines: What Shapes People's Reactions to Algorithmic Harm

arXiv: https://arxiv.org/abs/2304.02176 · HTML: https://mlsanity.com/paper/2304.02176/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 118 references that are in the map.
- First posted on arXiv 5 Apr 2023.

## Builds on (most cited first, 18 of 18)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [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)
- [To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making](https://mlsanity.com/paper/2102.09692/) (2021)
- [Artificial Intelligence: the global landscape of ethics guidelines](https://mlsanity.com/paper/1906.11668/) (2019)
- [Demystifying the Draft EU Artificial Intelligence Act](https://mlsanity.com/paper/2107.03721/) (2021)
- [Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination](https://mlsanity.com/paper/1408.6491/) (2014)
- [The Who in XAI: How AI Background Shapes Perceptions of AI Explanations](https://mlsanity.com/paper/2107.13509/) (2021)
- [Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning](https://mlsanity.com/paper/2202.05338/) (2022)
- [Human Perceptions on Moral Responsibility of AI: A Case Study in AI-Assisted Bail Decision-Making](https://mlsanity.com/paper/2102.00625/) (2021)
- [Dimensions of Diversity in Human Perceptions of Algorithmic Fairness](https://mlsanity.com/paper/2005.00808/) (2020)
- ["Look! It's a Computer Program! It's an Algorithm! It's AI!": Does Terminology Affect Human Perceptions and Evaluations of Algorithmic Decision-Making Systems?](https://mlsanity.com/paper/2108.11486/) (2021)
- [The Conflict Between Explainable and Accountable Decision-Making Algorithms](https://mlsanity.com/paper/2205.05306/) (2022)
- [Face Flashing: a Secure Liveness Detection Protocol based on Light Reflections](https://mlsanity.com/paper/1801.01949/) (2018)
- [Demographics and discussion influence views on algorithmic fairness](https://mlsanity.com/paper/1712.09124/) (2017)
- [The Conflict Between People's Urge to Punish AI and Legal Systems](https://mlsanity.com/paper/2003.06507/) (2020)
- [Delegation to autonomous agents promotes cooperation in collective-risk dilemmas](https://mlsanity.com/paper/2103.07710/) (2021)

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

- [Position: Towards Bidirectional Human-AI Alignment](https://mlsanity.com/paper/2406.09264/) (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)
- [Digital Companionship: Overlapping Uses of AI Companions and AI Assistants](https://mlsanity.com/paper/2510.15905/) (2025)
- [The AI Double Standard: Humans Judge All AIs for the Actions of One](https://mlsanity.com/paper/2412.06040/) (2024)
- [Superhuman Game AI Disclosure: Expertise and Context Moderate Effects on Trust and Fairness](https://mlsanity.com/paper/2503.15514/) (2025)

## Abstract (arXiv)

Artificial intelligence (AI) systems can cause harm to people. This research examines how individuals react to such harm through the lens of blame. Building upon research suggesting that people blame AI systems, we investigated how several factors influence people's reactive attitudes towards machines, designers, and users. The results of three studies (N = 1,153) indicate differences in how blame is attributed to these actors. Whether AI systems were explainable did not impact blame directed at them, their developers, and their users. Considerations about fairness and harmfulness increased blame towards designers and users but had little to no effect on judgments of AI systems. Instead, what determined people's reactive attitudes towards machines was whether people thought blaming them would be a suitable response to algorithmic harm. We discuss implications, such as how future decisions about including AI systems in the social and moral spheres will shape laypeople's reactions to AI-caused harm.