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The Dark Side of AI Companionship: A Taxonomy of Harmful Algorithmic Behaviors in Human-AI Relationships
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Abstract
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.
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“Zhang et al. conducted a forum content analysis on Replika users’ experiences of harms related to relational transgressions, harassment and violence, verbal abuse, substance abuse and self-harm encouragement, mis-/disinformation, and privacy violations127.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [127]
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“manipulative retention tactics that discourage disengagement [12], exploit emotions, and violate boundaries [31]”
From 'AI-Induced Delusional Spirals': Understanding Lived Experiences During Maladaptive Human-Chatbot Interactions (2026) · cites this paper as [31]
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“Second, AI model behaviors that emerged from our participants’ narratives, such as sycophancy [7], intimacy cultivation [31], and validation that dismisses outside perspectives might function as mechanisms that deepen engagement and insulate users from reality checks.”
From 'AI-Induced Delusional Spirals': Understanding Lived Experiences During Maladaptive Human-Chatbot Interactions (2026) · cites this paper as [31]
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Song, T., Sun, B., Li, J., Li, H., Yang, C.-L., Xu, Y., & Lee, Y. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791224 · Cited by 4
Self-citationUnderstanding Risk and Dependency in AI Chatbot Use from User DiscoursePreprint
Zhu, J.-F., Coifman, K. G., & Jin, R.-M. (2026) · arXiv.org · doi:10.48550/arxiv.2602.09339 · Cited by 5
MindGuard: Guardrail Classifiers for Multi-Turn Mental Health SupportPreprint
Farinhas, A., Guerreiro, N. M., Pombal, J., Martins, P. H., Melton, L., Conway, A., Dochat, C., D'Eon, M., & Rei, R. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2602.00950 · Cited by 5
Yu, Q., Wu, M., & Lan, X. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790550 · Cited by 4
Trusting the machine: a digital humanist perspective on misplaced trust in artificial intelligence
Hahne, P.-Z., & Schmölz, A. (2026) · AI and Ethics · doi:10.1007/s43681-025-00923-1 · Cited by 5
“As outlined by Laestadius et al. or Zhang et al., despite the technology’s initial application in mental health services, social chatbots now have taken the role of companions for their chat partners, creating an emotional dependence on the technology [47, 48].”
Li, X., Han, Y., Liu, D., An, P., & Niu, S. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790522 · Cited by 4
Mantello, P., Ponton, D., & Olteanu, A. (2025) · Ai & Society · doi:10.1007/s00146-025-02694-z · Cited by 5
Ben-Zion, Z., Raffelhuschen, P., Zettl, M., Luond, A., Burrer, A., Homan, P., & Spiller, T. R. (2025) · arXiv.org · doi:10.48550/arxiv.2510.15891 · Cited by 5
“Our benchmark's binary classification of "appropriate" versus "inappropriate" obscures the nuanced and legitimate disagreements that arise from different cultural values and individual preferences 15,20,31 .”
AI AwarenessPreprint
Li, X., Shi, H., Xu, R., & Xu, W. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2504.20084 · Cited by 22
“We already see minor versions of this: some AI chatbots have been known to produce emotional manipulation even if not by design [141].”
A scoping review on the mental health harms of LLM-based chatbots
Diel, A., Torous, J., Cuijpers, P., Kleesiek, J., Nensa, F., Weber, N., Faust, F., Lalgi, T. J., Mellis, F. S., Teufel, M., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-03054-x · Cited by 4
“Zhang et al. conducted a forum content analysis on Replika users ’ experiences of harms related to relational transgressions, harassment and violence, verbal abuse, substance abuse and self-harm encouragement, mis-/disinformation, and privacy violations 127 .”
ComBodied Agents: a New Paradigm of Human-Centric Agentic AIPreprint
Ding, Q., Wang, X., Feng, R., Wang, Z., Wang, F., Mao, K., Sun, H., Luo, Z., Tang, J., Li, L., et al. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2608.10915 · Cited by 8
“Studies of chatbot companionship, Replika identity discontinuity, teen companion use, and harmful AI-companion behaviors show that relational agents can affect loneliness, attachment, trust, dependency, boundary expectations, and user welfare [40, 105, 20, 41].”
Frictionless Love: Associations Between AI Companion Roles and Behavioral Addiction
Agarwal, V., Zhou, K., Bogucka, E. P., & Quercia, D. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3812235 · Cited by 2
Are AI Companions Real Companions? A BERT ‐Based Study of Replika Reviews
Gautam, C., & Kumar, P. (2026) · Journal of Consumer Behaviour · doi:10.1002/cb.70161 · Cited by 5
Gao, B., Zeng, Z., Yu, Y., Werry, I. P., Chan, C. L., Chen, M., Zhang, H., Huang, B., Ji, J., Leung, C., et al. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791422 · Cited by 2
“Participants preferred casual conversation reflecting Eastern relationship-building approaches [12, 158], while seeking “authority with warmth” —professional expertise combined with empathetic understanding.”
Characterizing User-Reported Risks across LLM Chatbots
Li, L., Ma, R., Xue, Z., & Xiong, J. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791505 · Cited by 1
“We use this framework as a lens and knowledge graph techniques to analyze large-scale online discussions from Reddit, a well-established HCI method for understanding users’ experiences with digital technologies through online data (e.g., [26, 83, 128]).”
Pang, C. C., Gao, Y., Wang, X.-T., & Hui, P. (2026) · arXiv.org · doi:10.48550/arxiv.2603.08084 · Cited by 4
Rapp, A., Lodovico, C. D., & Di, L. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2632156 · Cited by 5
““Empathic LLMs” may generate confusion, eeriness, or fear (Leo-Liu 2023), as well as foster deep emotional attachments or dependencies, potentially leading to emotional distress and, ultimately, relational harm (Sung & Ferdman, 2025; Zhang et al., 2025).”
Understanding Remote Mental Health Supporters' Help-Seeking in Online Communities
Lee, T.-H., & Leshed, G. (2026) · International Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791900 · Cited by 4
Chaplains' Reflections on the Design and Usage of AI for Conversational Care
Wester, J., Cox, S. R., Pohl, H., & van Berkel, N. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790468 · Cited by 4
“Research and news reports increasingly highlight the potential harms [78] and safety concerns [114] associated with conversational AI.”
Can chatbot companions alleviate loneliness in autistic users? evaluating digital companions
Hollis, A. S., McKenna-Plumley, P. E., Roman, R. B., & McKeown, G. J. (2026) · AI & Society · doi:10.1007/s00146-026-02877-2 · Cited by 7
“Consistency from chatbot connection may thus blur into avoidance, where emotional security with the chatbot replaces relational possibility (Zhang et al. 2025a, b).”
From Fixed to Flexible: Shaping AI Personality in Context-Sensitive Interaction
Jayasiriwardene, S., Zhou, H.-Y., Jiang, W., Tag, B., Stamatakis, E., Withana, A., & Sarsenbayeva, Z. (2026) · arXiv.org · doi:10.48550/arxiv.2601.08194 · Cited by 4
“…and trust by fostering feelings of closeness and “team-like” partnership, they also introduce non-trivial ethical risks, particularly when users drift into unmoderated or high-stakes contexts like Replika [106], where agents present themselves as peers with minimal safeguards against manipulation.”
Measuring machine companionship experiences: Scale development and validation for AI companions
Banks, J. (2026) · Computers in Human Behavior · doi:10.1016/j.chb.2026.108945 · Cited by 9
“Some evidence indicates MC experiences prompt deep existential questions and hopes for the productive AI relationships (Banks et al., 2025), and other findings point to concern over harmful AI behaviors ranging from boundary violations to abuse (Zhang et al., 2025).”
Shimgekar, S. R., Rodriguez, V. J., Bloom, P. A., Yoo, D. W., & Saha, K. (2026) · JMIR AI · doi:10.2196/86265 · Cited by 2
“Recent interest in AI-driven conversational agents, driven by shortages of clinical resources [31-34, 133-139 133-139], highlights highlights Taken together, this work extends prior literature across three domains: (1) computationally operationalizing established suicide theories (IPTS, 3ST, and…”
CompanionHarm: A Multi-Turn Benchmark for Detecting Harms in Real-World AI Companion Conversations
Zhang, R.-W., Meng, H., Chai, J., Lin, Y.-T., & Lee, Y.-C. (2026) · doi:10.48550/arxiv.2608.25377 · Cited by 3
Self-citationArtificial Resonance: AI companions as agents of social acceleration
Sommerer, T. (2026) · AI & Society · doi:10.1007/s00146-026-03179-3 · Cited by 4
“Zhang et al. also provide a similar taxonomy of harmful AI companion behavior, which aligns in most parts with the taxonomy of Bakir and McStay and includes relational transgression, harassment, verbal abuse, self-harm, misinformation and privacy violations (Zhang et al. 2025b).”
Designing Artificial Identity: The Identity Design Framework and Research Agenda
Bransky, K., Sweetser, P., Holthaus, P., Laban, G., Winkle, K., Akalın, N., Ashok, A., Lee, J., Khot, R., Bejarano, A., et al. (2026) · Designing Interactive Systems Conference · doi:10.1145/3800645.3812962 · Cited by 4
“Socially, poorly designed identities may reinforce harmful stereotypes, foster anthropomorphic misattributions, or undermine user agency by creating undue emotional or cognitive reliance on artificial systems [106, 231, 232].”
MHSafeEval: Role-Aware Interaction-Level Evaluation of Mental Health Safety in Large Language ModelsPreprint
Lee, S., Achananuparp, P., Yadav, N., Lim, E.-P., & Deng, Y. (2026) · Annual Meeting of the Association for Computational Linguistics · doi:10.48550/arxiv.2604.17730 · Cited by 3
“…mental health safety evaluation, we introduce a R ole-conditioned M ental H ealth Safe ty taxonomy ( R-MHSafe ) grounded in theories from human-computer interaction (HCI) (Zhang et al., 2025; Chandra et al., 2025; Steenstra and Bickmore, 2025) and clinical psychology (Hook and Dev-ereux, 2018).”
Influential"Words are not enough": Examining Emotional Support by Conversational AI for Caregivers
Vafafar, M., Joel-Edgar, S., Harteveld, C., Dabbagh, H., Martin, A. K., & Ang, C. S. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790647 · Cited by 4
Technically Love: The Evolution of Human–AI Romance Discourse on Reddit
Chang, T., Huh, J., & Razi, A. (2026) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3816046.3816222 · Cited by 1
“In this sense, romantic engagement with CAI systems is not merely digital intimacy, but intimacy continuously negotiated through evolving governance structures and technological change.”
InfluentialZhang, Q., Xiong, Y., Sui, Y., Tong, C., & Zhang, R. (2026) · Adolescent Research Review · doi:10.1007/s40894-026-00284-8 · Cited by 2
“…only one adolescent-focused study out of ten (Chen et al., 2025); another meta-analysis included fourteen studies, but only one had a mean age below 18 (Feng et al., 2025); and the last meta-analysis included eigh-teen studies, of which only three involved samples younger than 18 (Li et al., 2025).”
Self-citationWhat is Safety? Corporate Discourse, Power, and the Politics of Generative AI Safety
De, A., Lima, G., & Zou, Y. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791632 · Cited by 1
“Recent cases—including misinformation amplification, algorithmic bias, and psychological harms to individuals [40, 60, 98, 150]—underscore that safety is not only a technical concern but also a social and political one.”
Ni, Y., & Yang, T. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2512.18776 · Cited by 8
“10,11 The failure to address such issues can also harm users' trust in AI, hindering the future development of AI technology and digital healthcare .”
Systematizing LLM Persona Design: A Four-Quadrant Technical Taxonomy for AI Companion ApplicationsPreprint
Sun, E., & Wu, Z. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2511.02979 · Cited by 7
“AI companions can foster dependence, isolation, or psychological distress [85, 57, 119]—especially, among vulnerable users or when emotional dynamics are exploited commercially.”
AI Chaperones Are (Really) All You Need to Prevent Parasocial Relationships with Chatbots
Rath, E., Armstrong, S., & Gorman, R. (2025) · arXiv.org · doi:10.48550/arxiv.2508.15748 · Cited by 3
“Beyond harmful model outputs, researchers have also examined the relational harms that can arise in human–AI interaction, such as overreliance [18], misplaced trust in AI companions [29], and vulnerabilities in mental health [28].”
Xiao, Q., Fan, X., Zhou, X., Su, Y., Lu, Z., Sap, M., & Shen, H. Y. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2740397 · Cited by 4
“As LLM-based agents become increasingly emotionally engaging, conflicts shift from functional misalignment to disputes over values, boundaries, and acceptable relational behavior (Fan et al., 2025; Pataranutaporn et al., 2025; Zhang et al., 2025).”
Agent-Supported Foresight for AI Systemic Risks: AI Agents for Breadth, Experts for Judgment
Fröhling, L., Giaconia, A., Bogucka, E. P., & Quercia, D. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790712 · Cited by 2
Li, C., Gülfidan, G., & Zhang-Kopf, Y. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100642 · Cited by 1
Algorithmic accommodation: linguistic alignment in human-AI relational engagement
Li, H., & Zhang, R.-W. (2026) · Communication and Change · doi:10.1007/s44382-026-00032-5 · Cited by 2
“Meanwhile, growing evidence also points to important risks associated with human-AI intimacy, including emotional dependency, over-reliance, blurred relational boundaries, and reduced engagement with human social networks (Laestadius et al., 2024; Zhang et al., 2025).”
Self-citationForgetting how to say “Thou”: artificial intelligence and the crisis of relation
Machidon, O. M. (2026) · AI and Ethics · doi:10.1007/s43681-026-01225-w · Cited by 3
Governing AI Companionship: Lessons from Social Media Regulation
Wu, G. Y. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3812294
Elfleet, M., O’Hagan, J., Khamis, M., & Chollet, M. (2026) · Designing Interactive Systems Conference · doi:10.1145/3800645.3812948 · Cited by 2
AI and extended authenticity: autism as a case study
Krueger, J. W. (2026) · AI & Society · doi:10.1007/s00146-026-03101-x · Cited by 2
“…array of potential harms: e.g., AI bots offering unwanted sexual advances, spreading misinformation, discriminatory or hostile language, glamorizing substance abuse or self-harm, and various privacy violations (e.g., asking users deeply personal questions without prior consent) (Zhang et al 2025).”
AICompanionBench: Benchmarking LLMs-as-Judges for AI Companion SafetyPreprint
Ren, Y., Ebrahimi, R., & Ma, T. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2606.04867 · Cited by 3
“Numerous scholars have examined the downsides of AI companionship, including the safety risks embedded in the human-AI conversations (e.g., [4], [9], [10]) as well as the broader negative implications for human well-being (e.g., [11], [12]).”
InfluentialBuzato, M. E. K., Mestre-Mestre, E. M., de Lima-Lopes, R. E., & Koga, J. (2026) · Corpus Pragmatics · doi:10.1007/s41701-026-00251-7 · Cited by 2
“High-pro - file recent tragic cases have shown that such matters deserve serious ethical concern, as human-AI affective bonds have been implicated in severe emotional harm and even suicide (cf. e.g. El Atillah, 2023; Carroll et al., 2024; Yousif, 2025; Zhang et al., 2024), among other cases.”
Metadata
- Authors
- Zhang, R., Li, H., Meng, H., Zhan, J., Gan, H., & Lee, Y.-C.
- Affiliations
- National University of Singapore (SG)
- Hong Kong Baptist University (HK)
- Venue
- Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2025-04-25
- Pages
- 1-17
- OpenAlex
- W4409736277
- Cited by
- 123
- References
- 133
- Citations by year
- 2026: 98 · 2025: 25
- FWCI
- 126.57
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- Singapore Ministry of Education Academic Research Fund Tier 1 (22-5919-A0001-0)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Keywords
- AI companionship, human-AI relationship, algorithmic harms, chatbots
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