"Even GPT Can Reject Me": Conceptualizing Abrupt Refusal Secondary Harm (ARSH) and Reimagining Psychological AI Safety with Compassionate Completion Standard (CCS)
- URL: http://arxiv.org/abs/2512.18776v1
- Date: Sun, 21 Dec 2025 15:31:15 GMT
- Title: "Even GPT Can Reject Me": Conceptualizing Abrupt Refusal Secondary Harm (ARSH) and Reimagining Psychological AI Safety with Compassionate Completion Standard (CCS)
- Abstract summary: We argue that abrupt refusals can rupture perceived relational continuity, evoke feelings of rejection or shame, and discourage future help seeking.<n>We propose a design hypothesis, the Compassionate Completion Standard, that maintains safety constraints while preserving relational coherence.<n>This viewpoint contributes a timely conceptual framework, articulates a testable design hypothesis, and outlines a coordinated research agenda for improving psychological safety in human AI interaction.
- Score: 10.377213441117618
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large Language Models (LLMs) and AI chatbots are increasingly used for emotional and mental health support due to their low cost, immediacy, and accessibility. However, when safety guardrails are triggered, conversations may be abruptly terminated, introducing a distinct form of emotional disruption that can exacerbate distress and elevate risk among already vulnerable users. As this phenomenon gains attention, this viewpoint introduces Abrupt Refusal Secondary Harm (ARSH) as a conceptual framework to describe the psychological impacts of sudden conversational discontinuation caused by AI safety protocols. Drawing on counseling psychology and communication science as conceptual heuristics, we argue that abrupt refusals can rupture perceived relational continuity, evoke feelings of rejection or shame, and discourage future help seeking. To mitigate these risks, we propose a design hypothesis, the Compassionate Completion Standard (CCS), a refusal protocol grounded in Human Centered Design (HCD) that maintains safety constraints while preserving relational coherence. CCS emphasizes empathetic acknowledgment, transparent boundary articulation, graded conversational transition, and guided redirection, replacing abrupt disengagement with psychologically attuned closure. By integrating awareness of ARSH into AI safety design, developers and policymakers can reduce preventable iatrogenic harm and advance a more psychologically informed approach to AI governance. Rather than presenting incremental empirical findings, this viewpoint contributes a timely conceptual framework, articulates a testable design hypothesis, and outlines a coordinated research agenda for improving psychological safety in human AI interaction.
Related papers
- Disrupted Companionship: A Risk Assessment Framework and Cross-Platform Quantitative Analysis of Psychosocial Responses to AI Companion Disruptions [18.185694912673824]
We study AI companion disruptions: platform changes that alter or terminate users' ongoing companionship with an AI.<n>We develop a taxonomy of six disruption types, identify three broad reasons for disruption, and propose a risk-assessment framework.<n>Our findings provide a cross-platform characterization of AI companion disruptions, quantitative evidence of their psychosocial impacts, and a prospective framework for assessing their potential risks.
arXiv Detail & Related papers (2026-09-15T09:37:29Z) - An Echo Chamber of One: Should AI Psychosis Be a Distinct Clinical Entity? [10.277675420730196]
"AI psychosis" has entered public and clinical discourse as a label for the onset or exacerbation of psychotic symptoms.<n>We examine whether AI-associated psychosis warrants recognition as a distinct clinical entity.
arXiv Detail & Related papers (2026-08-25T00:53:46Z) - Cognitive Pivot Points and Visual Anchoring: Unveiling and Rectifying Hallucinations in Multimodal Reasoning Models [65.57985131861399]
hallucinations are strongly correlated with cognitive bifurcation points that often exhibit high entropy states.<n>We propose V-STAR, a lightweight, holistic training paradigm designed to internalize visually aware reasoning capabilities.
arXiv Detail & Related papers (2026-04-11T13:59:05Z) - Technosocial risks of ideal emotion recognition technologies: A defense of the (social) value of emotional expressions [51.56484100374058]
I argue that the appeal of such systems rests on a misunderstanding of the social functions emotional expression.<n>ERTs threaten this expressive space by collapsing epistemic friction, displacing meaning with technology-mediated affective profiles, and narrowing the space for aspirational and role-sensitive expressions.<n>I argue that, although it is intuitive to think that increasing accuracy would legitimise such systems, in the case of ERTs accuracy does not straightforwardly justify their deployment, and may, in some contexts, provide a reason for regulatory restraint.
arXiv Detail & Related papers (2026-02-09T14:20:42Z) - EmoRAG: Evaluating RAG Robustness to Symbolic Perturbations [57.97838850473147]
Retrieval-Augmented Generation (RAG) systems are increasingly central to robust AI.<n>Our study unveils a critical, overlooked vulnerability: their susceptibility to subtle symbolic perturbations.<n>We demonstrate that injecting a single emoticon into a query makes it nearly 100% likely to retrieve semantically unrelated texts.
arXiv Detail & Related papers (2025-12-01T06:53:49Z) - AI Deception: Risks, Dynamics, and Controls [153.71048309527225]
This project provides a comprehensive and up-to-date overview of the AI deception field.<n>We identify a formal definition of AI deception, grounded in signaling theory from studies of animal deception.<n>We organize the landscape of AI deception research as a deception cycle, consisting of two key components: deception emergence and deception treatment.
arXiv Detail & Related papers (2025-11-27T16:56:04Z) - Towards Emotionally Intelligent and Responsible Reinforcement Learning [0.40719854602160227]
We propose a Responsible Reinforcement Learning framework that integrates emotional and contextual understanding with ethical considerations.<n>We introduce a multi-objective reward function that balances short-term behavioral engagement with long-term user well-being.<n>We discuss the implications of this approach for human-centric domains such as behavioral health, education, and digital therapeutics.
arXiv Detail & Related papers (2025-11-13T18:09:37Z) - Embracing Trustworthy Brain-Agent Collaboration as Paradigm Extension for Intelligent Assistive Technologies [51.93721053301417]
This paper argues that the field is poised for a paradigm extension from Brain-Computer Interfaces to Brain-Agent Collaboration.<n>We emphasize reframing agents as active and collaborative partners for intelligent assistance rather than passive brain signal data processors.
arXiv Detail & Related papers (2025-10-25T00:25:45Z) - Human-AI Interactions: Cognitive, Behavioral, and Emotional Impacts [0.0]
Potential risks of overreliance, cognitive offloading, social and emotional manipulation, and the nuanced degradation of human agency and judgment are highlighted.<n> Observations seem to suggest that while AI can substantially enhance memory, creativity, and engagement, it also introduces risks such as diminished critical thinking, skill erosion, and increased anxiety.<n>This paper aims to underscore the need for responsible and context-aware AI design, highlighting gaps for longitudinal research and grounded evaluation frameworks to balance benefits with emerging human-centric risks.
arXiv Detail & Related papers (2025-10-20T17:06:46Z) - Mental Health Impacts of AI Companions: Triangulating Social Media Quasi-Experiments, User Perspectives, and Relational Theory [18.716972390545703]
We examined how engaging with AICCs shaped wellbeing and how users perceived these experiences.<n>Findings revealed mixed effects -- greater affective and grief expression, readability, and interpersonal focus.<n>We offer design implications for AI companions that scaffold healthy boundaries, support mindful engagement, support disclosure without dependency, and surface relationship stages.
arXiv Detail & Related papers (2025-09-26T15:47:37Z) - Oyster-I: Beyond Refusal -- Constructive Safety Alignment for Responsible Language Models [93.5740266114488]
Constructive Safety Alignment (CSA) protects against malicious misuse while actively guiding vulnerable users toward safe and helpful results.<n>Oy1 achieves state-of-the-art safety among open models while retaining high general capabilities.<n>We release Oy1, code, and the benchmark to support responsible, user-centered AI.
arXiv Detail & Related papers (2025-09-02T03:04:27Z) - Emotion Transfer with Enhanced Prototype for Unseen Emotion Recognition in Conversation [64.70874527264543]
We introduce the Unseen Emotion Recognition in Conversation (UERC) task for the first time.<n>We propose ProEmoTrans, a prototype-based emotion transfer framework.<n>ProEmoTrans shows promise but still faces key challenges.
arXiv Detail & Related papers (2025-08-27T03:16:16Z) - Mitigating Societal Cognitive Overload in the Age of AI: Challenges and Directions [0.9906787204170321]
Societal cognitive overload, driven by the deluge of information and complexity in the AI age, poses a critical challenge to human well-being and societal resilience.<n>This paper argues that mitigating cognitive overload is not only essential for improving present-day life but also a crucial prerequisite for navigating the potential risks of advanced AI.
arXiv Detail & Related papers (2025-04-28T17:06:30Z) - EmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental Health Safety [42.052840895090284]
EmoAgent is a multi-agent AI framework designed to evaluate and mitigate mental health hazards in human-AI interactions.<n>EmoEval simulates virtual users, including those portraying mentally vulnerable individuals, to assess mental health changes before and after interactions with AI characters.<n>EmoGuard serves as an intermediary, monitoring users' mental status, predicting potential harm, and providing corrective feedback to mitigate risks.
arXiv Detail & Related papers (2025-04-13T18:47:22Z) - Enhancing Mental Health Support through Human-AI Collaboration: Toward Secure and Empathetic AI-enabled chatbots [0.0]
This paper explores the potential of AI-enabled chatbots as a scalable solution.
We assess their ability to deliver empathetic, meaningful responses in mental health contexts.
We propose a federated learning framework that ensures data privacy, reduces bias, and integrates continuous validation from clinicians to enhance response quality.
arXiv Detail & Related papers (2024-09-17T20:49:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.