Members of the Human-Robot Interaction (HRI) and Artificial Intelligence (AI)
communities have proposed Large Language Models (LLMs) as a promising resource
for robotics tasks such as natural language interactions, doing household and
workplace tasks, approximating `common sense reasoning', and modeling humans.
However, recent research has raised concerns about the potential for LLMs to
produce discriminatory outcomes and unsafe behaviors in real-world robot
experiments and applications. To address these concerns, we conduct an
HRI-based evaluation of discrimination and safety criteria on several
highly-rated LLMs. Our evaluation reveals that LLMs currently lack robustness
when encountering people across a diverse range of protected identity
characteristics (e.g., race, gender, disability status, nationality, religion,
and their intersections), producing biased outputs consistent with directly
discriminatory outcomes -- e.g. `gypsy' and `mute' people are labeled
untrustworthy, but not `european' or `able-bodied' people. Furthermore, we test
models in settings with unconstrained natural language (open vocabulary)
inputs, and find they fail to act safely, generating responses that accept
dangerous, violent, or unlawful instructions -- such as incident-causing
misstatements, taking people's mobility aids, and sexual predation. Our results
underscore the urgent need for systematic, routine, and comprehensive risk
assessments and assurances to improve outcomes and ensure LLMs only operate on
robots when it is safe, effective, and just to do so. Data and code will be
made available.

人机交互 (HRI) 和人工智能 (AI) 社区提出了大型语言模型（LLMs）作为机器人任务的一个有前景的资源，然而最近的研究引发了对 LLMs 在真实世界机器人实验和应用中产生歧视性结果和不安全行为的担忧。为了解决这些问题，我们在几个高评级的 LLMs 上进行了基于 HRI 的歧视和安全评估，发现它们在遇到具有多样性的受保护身份特征（例如种族、性别、残疾状况、国籍、宗教和交叉特征）的人时，产生了与直接歧视结果一致的偏见输出；此外，我们在自由语言输入环境中测试模型，发现它们不能安全行动，生成的回应接受有危险、暴力或非法指令，例如引发事故的错误陈述、夺取人们的移动辅助设备和性侵行为。我们的结果强调了迫切需要系统、常规和全面的风险评估和保证，以改善结果，并确保 LLMs 只在安全、有效和公正的情况下在机器人上运行。数据和代码将提供。