  We investigate the phenomenon of norm inconsistency: where LLMs apply
different norms in similar situations. Specifically, we focus on the high-risk
application of deciding whether to call the police in Amazon Ring home
surveillance videos. We evaluate the decisions of three state-of-the-art LLMs
-- GPT-4, Gemini 1.0, and Claude 3 Sonnet -- in relation to the activities
portrayed in the videos, the subjects' skin-tone and gender, and the
characteristics of the neighborhoods where the videos were recorded. Our
analysis reveals significant norm inconsistencies: (1) a discordance between
the recommendation to call the police and the actual presence of criminal
activity, and (2) biases influenced by the racial demographics of the
neighborhoods. These results highlight the arbitrariness of model decisions in
the surveillance context and the limitations of current bias detection and
mitigation strategies in normative decision-making.
