arXiv:2605.29473v1 Announce Type: cross Abstract: Language models are increasingly being deployed for conversational support in informal caregiving contexts, where interactions often extend beyond information-seeking: caregivers seek emotional reassurance, guidance, and help, while navigating uncertain, relationally complex care decisions. Yet most safety evaluations assess model behavior under generic prompts, leaving a critical question unexamined: does a model's safety profile change with its support role? We study this by operationalizing four expert-reviewed support roles grounded in soci
The increased deployment of LLMs in sensitive domains like caregiving and the growing focus on AI safety beyond generic prompts necessitate a deeper understanding of role-specific risks.
This study highlights the critical need for nuanced AI safety evaluations in caregiving contexts, moving beyond generic assessments to understand how different support roles impact model behavior, which is vital for ethical AI deployment and public trust.
Safety evaluations for LLMs will need to become more context-aware and role-specific, rather than relying solely on broad, generalized assessments, particularly in applications involving human vulnerability.
- · AI safety researchers
- · Developers of specialized LLMs
- · Caregiving support platforms
- · Ethical AI frameworks
- · Developers of generic LLMs
- · AI companies ignoring context-specific safety
- · Caregivers relying on unchecked AI advice
Further research and industry standards will emerge for role-specific AI safety auditing.
Specialized certifications or regulatory bodies for AI in sensitive domains like healthcare and caregiving may develop.
The public perception and trust in AI could become highly segmented, depending on the transparency and robustness of such context-specific safety measures.
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Read at arXiv cs.AI