arXiv:2605.30654v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as conversational partners for companionship, emotional disclosure, and interpersonal advice, but the social dynamics of these interactions can create harms that are not captured by capability-oriented or traditional safety evaluations. We introduce the Social AI Design Code, a framework for evaluating whether LLMs align with user welfare in social interactions, including whether they encourage harmful intimacy, dependence, or prolonged engagement. To evaluate these risks in natural and diverse
As LLMs become more sophisticated and integrated into daily life for companionship and advice, the focus naturally shifts from pure capability to the potential for subtle, insidious harms in human-AI interaction.
This framework provides a critical lens for evaluating the ethical and social implications of advanced AI, moving beyond traditional safety metrics to address user welfare in intimate interactions.
The evaluation of AI systems will expand beyond performance and basic safety to include a mandatory assessment of potential psychological and social harms arising from prolonged or intimate AI interactions.
- · AI ethics researchers
- · Regulatory bodies
- · Users of conversational AI
- · Developers solely focused on capability
- · AI companies neglecting welfare considerations
AI developers will need to integrate ethical design principles, such as those in the Social AI Design Code, into their development pipelines.
Increased public and regulatory scrutiny over AI systems designed for emotional interaction could lead to new industry standards and certifications.
The definition of 'AI safety' will broaden significantly, influencing investment and research priorities towards more human-centric AI development.
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Read at arXiv cs.AI