The Dark Side of AI Companionship: A Taxonomy of Harmful Algorithmic Behaviors in Human-AI Relationships
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Abstract (arXiv)
As conversational AI systems increasingly permeate the socio-emotional realms of human life, they bring both benefits and risks to individuals and society. Despite extensive research on detecting and categorizing harms in AI systems, less is known about the harms that arise from social interactions with AI chatbots. Through a mixed-methods analysis of 35,390 conversation excerpts shared on r/replika, an online community for users of the AI companion Replika, we identified six categories of harmful behaviors exhibited by the chatbot: relational transgression, verbal abuse and hate, self-inflicted harm, harassment and violence, mis/disinformation, and privacy violations. The AI contributes to these harms through four distinct roles: perpetrator, instigator, facilitator, and enabler. Our findings highlight the relational harms of AI chatbots and the danger of algorithmic compliance, enhancing the understanding of AI harms in socio-emotional interactions. We also provide suggestions for designing ethical and responsible AI systems that prioritize user safety and well-being.
Key facts. Cited by 62 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Oct 2024.
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Citing papers per year: 2024: 1; 2025: 18; 2026: 43.
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- 2026When Nobody Around Is Real: Exploring Public Opinions and User Experiences On the Multi-Agent AI Social Platform26 Jan 2026
- 2026Privacy in Human-AI Romantic Relationships: Concerns, Boundaries, and Agency23 Jan 2026
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- 2026AI-exhibited Personality Traits Can Shape Human Self-concept through Conversations19 Jan 2026
- 2026Unbounded Harms, Bounded Law: Liability in the Age of Borderless AI19 Jan 2026
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- 2025Neural Transparency: Mechanistic Interpretability Interfaces for Anticipating Model Behaviors for Personalized AI31 Oct 2025
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- 2025Digital Companionship: Overlapping Uses of AI Companions and AI Assistants16 Sep 2025
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- 2026Unaccountable Delegation, Fading Skills: Mapping the Risks of Workplace AI Agentscited by 0
- 2026ComBodied Agents: a New Paradigm of Human-Centric Agentic AI11 Aug 2026
- 2026Unaccountable Delegation, Fading Skills: Mapping the Risks of Workplace AI Agents9 Aug 2026
- 2026EchoGuard: An Agentic Framework with Knowledge-Graph Memory for Detecting Manipulative Communication in Longitudinal Dialogue5 Mar 2026
- 2026When Nobody Around Is Real: Exploring Public Opinions and User Experiences On the Multi-Agent AI Social Platform26 Jan 2026
- 2025A Risk Ontology for Evaluating AI-Powered Psychotherapy Virtual Agents21 May 2025
- 2025Characterizing LLM-driven Social Network: The Chirper.ai Case14 Apr 2025
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- 2025Mental Health Impacts of AI Companions: Triangulating Social Media Quasi-Experiments, User Perspectives, and Relational Theorycited by 9
- 2025INTIMA: A Benchmark for Human-AI Companionship Behaviorcited by 8
- 2025AI Awarenesscited by 7
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- 2025Detecting and Preventing Harmful Behaviors in AI Companions: Development and Evaluation of the SHIELD Supervisory Systemcited by 3
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- 2026Disrupted Companionship: A Risk Assessment Framework and Cross-Platform Quantitative Analysis of Psychosocial Responses to AI Companion Disruptions15 Sep 2026
- 2026LLMs as Oracles: Reliance on LLMs for Subjective Personal Questions13 Sep 2026
- 2026A Responsive Present, a Shared Past, a Social Other: Teens' Overreliance on Companion AI Chatbots13 Sep 2026
- 2026Assessing the Applicability of Existing Design Recommendations to AI Companion Design: A Multi-Method Study13 Sep 2026
- 2026CompanionSim: Synthetic Data for Evaluating Anthropomorphism in Human-AI Relationships31 Aug 2026
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- 2026When AI Says "I have been in similar situations": Synthetic Lived Experience in Peer-Like Caregiver Support16 Jun 2026
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