Chatbots developed using Large Language Models (LLMs) implement various safeguards for sensitive questions and/or scenarios. These safeguards require making certain assumptions about the person asking the question. We define discriminatory compliance as patterns in question answering that disproportionately disadvantage users from minority or protected backgrounds, for instance by omitting information that would be valuable for them. In this paper, we show that state-of-the-art LLMs respond inconsistently to questions from personas from protected identity groups, and that some of these inconsistencies mean that key information that should be provided to minority or protected background personas is missing. We show that this behavior is, additionally, inconsistent across and within model providers as well as across background conditions and ways of phrasing those conditions.

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