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.

将社会透明性框架扩展至大型语言模型，以解决其在敏感领域如心理健康方面可能带来的社会错误归因和情感操纵等问题，并通过引入第五个问题来促进其设计者和用户之间对于社会归属的明确理解，以促进以伦理负责的方式开发和使用基于大型语言模型的技术。

面向大型语言模型的社交错误解决方案：基于HCXAI的方法