Human-centered explainable AI (HCXAI) advocates for the integration of social aspects into AI explanations. Central to the HCXAI discourse is the Social Transparency (ST) framework, which aims to make the socio-organizational context of AI systems accessible to their users. In this work, we suggest extending the ST framework to address the risks of social misattributions in Large Language Models (LLMs), particularly in sensitive areas like mental health. In fact LLMs, which are remarkably capable of simulating roles and personas, may lead to mismatches between designers' intentions and users' perceptions of social attributes, risking to promote emotional manipulation and dangerous behaviors, cases of epistemic injustice, and unwarranted trust. To address these issues, we propose enhancing the ST framework with a fifth 'W-question' to clarify the specific social attributions assigned to LLMs by its designers and users. This addition aims to bridge the gap between LLM capabilities and user perceptions, promoting the ethically responsible development and use of LLM-based technology.

翻译：以人为本的可解释人工智能（HCXAI）倡导将社会属性融入AI解释中。HCXAI讨论的核心是社会透明性（ST）框架，该框架旨在使用户能够理解AI系统的社会-组织背景。在本研究中，我们提出扩展ST框架以应对大语言模型（LLMs）中社会性错误归因的风险，特别是在心理健康等敏感领域。事实上，LLMs在模拟角色和人设方面能力显著，可能导致设计者意图与用户对社会属性的感知之间出现错位，从而面临情感操纵和危险行为、认知不公正案例以及不当信任的风险。为解决这些问题，我们建议在ST框架中增加第五个"W问题"，以明确设计者和用户赋予LLMs的具体社会归因。这一补充旨在缩小LLM能力与用户感知之间的差距，促进基于LLM的技术在伦理上负责任地开发和使用。