paper-with-me

홈 › Papers

Regulating Artificial Intimacy: From Locks and Blocks to Relational Accountability

2026-04-20 · Henry Fraser, Jessica M. Szczuka, Raffaele F. Ciriello arxiv

A series of high-profile tragedies involving companion chatbots has triggered an unusually rapid regulatory response. Several jurisdictions, including Australia, California, and New York, have introduced enforceable regulation, while regulators elsewhere have signaled growing concern about risks posed by companion chatbots, particularly to children. In parallel, leading providers, notably OpenAI, appear to have strengthened their self-regulatory approaches. Drawing on legal textual analysis and insights from regulatory theory, psychology, and information systems research, this paper critically examines these recent interventions. We examine what is regulated and who is regulated, identifying regulatory targets, scope, and modalities. We classify interventions by method and priority, showing how emerging regimes combine "locks and blocks", such as access gating and content moderation, with measures addressing toxic relationship features and process-based accountability requirements. We argue that effective regulation of companion chatbots must integrate all three dimensions. More, however, is required. Current regimes tend to focus on discrete harms, narrow conceptions of vulnerability, or highly specified accountability processes, while failing to confront deeper power asymmetries between providers and users. Providers of companion chatbots increasingly control artificial intimacy at scale, creating unprecedented opportunities for control through intimacy. We suggest that a general, open-ended duty of care would be an important first step toward constraining that power and addressing a fundamental source of chatbot risk. The paper contributes to debates on companion chatbot regulation and is relevant to regulators, platform providers, and scholars concerned with digital intimacy, law and technology, and fairness, accountability, and transparency in sociotechnical systems.

📄 PDF Abstract BibTeX arXiv:2604.18893

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

The Limits of Artificial Companionship

2026-04-26 · Mauricio Figueroa arxiv

This Article argues that conversations with companion chatbot should be subject to a clear structural distinction between commercial and non-commercial contexts. The insertion of undisclosed promotional content into affe…

Not a Silver Bullet for Loneliness: How Attachment and Age Shape Intimacy with AI Companions

2026-02-12 · Raffaele Ciriello, Uri Gal, Ofir Turel arxiv

Artificial intelligence (AI) companions are increasingly promoted as solutions for loneliness, often overlooking how personal dispositions and life-stage conditions shape artificial intimacy. Because intimacy is a primar…

Causal Inference

Distributed Associative Memory Network with Memory Refreshing Loss

2020-07-21 · Taewon Park, Inchul Choi, Minho Lee

Despite recent progress in memory augmented neural network (MANN) research, associative memory networks with a single external memory still show limited performance on complex relational reasoning tasks. Especially the c…

MemorizationQuestion AnsweringRelational Reasoning

Relational inductive bias for physical construction in humans and machines

2018-06-04 · Jessica B. Hamrick, Kelsey R. Allen, Victor Bapst, Tina Zhu 외

While current deep learning systems excel at tasks such as object classification, language processing, and gameplay, few can construct or modify a complex system such as a tower of blocks. We hypothesize that what these …

Deep Reinforcement LearningInductive BiasObjectReinforcement Learning

Distributed Associative Memory Network with Association Reinforcing Loss

2021-01-01 · Taewon Park, Inchul Choi, Minho Lee

Despite recent progress in memory augmented neural network research, associative memory networks with a single external memory still show limited performance on complex relational reasoning tasks. The main reason for thi…

MemorizationRelational Reasoning