Anthropomorphization is the tendency to attribute human-like traits to
non-human entities. It is prevalent in many social contexts -- children
anthropomorphize toys, adults do so with brands, and it is a literary device.
It is also a versatile tool in science, with behavioral psychology and
evolutionary biology meticulously documenting its consequences. With widespread
adoption of AI systems, and the push from stakeholders to make it human-like
through alignment techniques, human voice, and pictorial avatars, the tendency
for users to anthropomorphize it increases significantly. We take a dyadic
approach to understanding this phenomenon with large language models (LLMs) by
studying (1) the objective legal implications, as analyzed through the lens of
the recent blueprint of AI bill of rights and the (2) subtle psychological
aspects customization and anthropomorphization. We find that anthropomorphized
LLMs customized for different user bases violate multiple provisions in the
legislative blueprint. In addition, we point out that anthropomorphization of
LLMs affects the influence they can have on their users, thus having the
potential to fundamentally change the nature of human-AI interaction, with
potential for manipulation and negative influence. With LLMs being
hyper-personalized for vulnerable groups like children and patients among
others, our work is a timely and important contribution. We propose a
conservative strategy for the cautious use of anthropomorphization to improve
trustworthiness of AI systems.

研究人员发现，人们将人性化特征赋予 AI 系统的倾向会严重影响其在法律层面上的合规性，并对人类与 AI 之间的互动方式产生根本性的改变，实现潜在的操纵和负面影响。为了提高 AI 系统的可靠性，我们提出了一种谨慎使用人性化策略的保守策略。