mlsanityData to 7 Oct 2026

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

arXiv 2304.02176 ↗First posted 5 Apr 2023cs.CY cs.AI cs.HCcited by 5

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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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 18 of its 118 references that are in the map. First posted on arXiv 5 Apr 2023.

Builds on

18 of 118 references.

References · 18

  1. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  2. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  3. 2020Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performancecited by 396
  4. 2021What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Researchcited by 119
  5. 2021To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-makingcited by 118
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  7. 2014Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discriminationcited by 83
  8. 2021Demystifying the Draft EU Artificial Intelligence Actcited by 82
  9. 2021The Who in XAI: How AI Background Shapes Perceptions of AI Explanationscited by 48
  10. 2022Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learningcited by 47
  11. 2021Human Perceptions on Moral Responsibility of AI: A Case Study in AI-Assisted Bail Decision-Makingcited by 23
  12. 2020Dimensions of Diversity in Human Perceptions of Algorithmic Fairnesscited by 13
6 more
  1. 2021"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?cited by 13
  2. 2022The Conflict Between Explainable and Accountable Decision-Making Algorithmscited by 13
  3. 2018Face Flashing: a Secure Liveness Detection Protocol based on Light Reflectionscited by 7
  4. 2017Demographics and discussion influence views on algorithmic fairnesscited by 4
  5. 2020The Conflict Between People's Urge to Punish AI and Legal Systemscited by 4
  6. 2021Delegation to autonomous agents promotes cooperation in collective-risk dilemmascited by 3

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Cited by year

Citing papers per year: 2024: 3; 2025: 2.

5 papers.

  1. 2024Position: Towards Bidirectional Human-AI Alignmentcited by 15
  2. 2024Perceptions of Sentient AI and Other Digital Minds: Evidence from the AI, Morality, and Sentience (AIMS) Surveycited by 15
  3. 2025Digital Companionship: Overlapping Uses of AI Companions and AI Assistantscited by 7
  4. 2024The AI Double Standard: Humans Judge All AIs for the Actions of Onecited by 6
  5. 2025Superhuman Game AI Disclosure: Expertise and Context Moderate Effects on Trust and Fairnesscited by 0