  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.
