Blaming Humans and Machines: What Shapes People's Reactions to Algorithmic Harm
Gabriel Lima, Nina Grgic-Hlaca, Meeyoung Cha
Abstract
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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Cited by top-tier papers8
- The Dark Side of AI Companionship: A Taxonomy of Harmful Algorithmic Behaviors in Human-AI RelationshipsRenwen Zhang, Han Li, Han Meng, Jinyuan Zhan et al.CHI 2025 · 122 citations
- Debate Chatbots to Facilitate Critical Thinking on YouTube: Social Identity and Conversational Style Make A DifferenceThitaree Tanprasert, Sidney S. Fels, Luanne Sinnamon, Dongwook YoonCHI 2024 · 51 citations
- Perceptions of Sentient AI and Other Digital Minds: Evidence from the AI, Morality, and Sentience (AIMS) SurveyJacy Reese Anthis, Janet V. T. Pauketat, Ali Ladak, Aikaterina ManoliCHI 2025 · 24 citations
- Public Opinions About Copyright for AI-Generated Art: The Role of Egocentricity, Competition, and ExperienceGabriel Lima, Nina Grgic-Hlaca, Elissa M. RedmilesCHI 2025 · 18 citations
- The AI Double Standard: Humans Judge All AIs for the Actions of OneAikaterina Manoli, Janet V. T. Pauketat, Jacy Reese AnthisCSCW 2025 · 10 citations
Builds on10
- To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-makingZana Buçinca, Maja Barbara Malaya, Krzysztof Z. GajosCSCW 2021 · 962 citations
- Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team PerformanceGagan Bansal, Tongshuang Wu, Joyce Zhou, Raymond Fok et al.CHI 2021 · 713 citations
- The Who in XAI: How AI Background Shapes Perceptions of AI ExplanationsUpol Ehsan, Samir Passi, Q. Vera Liao, Larry Chan et al.CHI 2024 · 121 citations
- "Because AI is 100% right and safe": User Attitudes and Sources of AI Authority in IndiaShivani Kapania, Oliver Siy, Gabe Clapper, Azhagu Meena SP et al.CHI 2022 · 90 citations
- Human Perceptions on Moral Responsibility of AI: A Case Study in AI-Assisted Bail Decision-MakingGabriel Lima, Nina Grgic-Hlaca, Meeyoung ChaCHI 2021 · 71 citations
Related papers
- Guilty Artificial Minds: Folk Attributions of Mens Rea and Culpability to Artificially Intelligent AgentsMichael T. Stuart, Markus KneerCSCW 2021 · 48 citations
- Responsibility Attribution in Human Interactions with Everyday AI SystemsJoe Brailsford, Frank Vetere, Eduardo VellosoCHI 2025 · 10 citations
- The Potential of Diverse Youth as Stakeholders in Identifying and Mitigating Algorithmic Bias for a Future of Fairer AIJaemarie Solyst, Ellia Yang, Shixian Xie, Amy Ogan et al.CSCW 2023 · 66 citations
- Owning Mistakes Sincerely: Strategies for Mitigating AI ErrorsAmama Mahmood, Jeanie W. Fung, Isabel Won, Chien-Ming HuangCHI 2022 · 50 citations
- People May Punish, But Not Blame RobotsMinha Lee, Peter A. M. Ruijten, Lily Frank, Yvonne de Kort et al.CHI 2021 · 27 citations
