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With the growing popularity of dialogue agents based on large language models(LLMs), urgent attention has been drawn to finding ways to ensure theirbehaviour is ethical and appropriate. These are largely interpreted in terms ofthe 'HHH' criteria: making outputs more helpful and honest, and avoidingharmful (biased, toxic, or inaccurate) statements. Whilst this semantic focusis useful from the perspective of viewing LLM agents as mere mediums forinformation, it fails to account for pragmatic factors that can make the sameutterance seem more or less offensive or tactless in different socialsituations. We propose an approach to ethics that is more centred on relationaland situational factors, exploring what it means for a system, as a socialactor, to treat an individual respectfully in a (series of) interaction(s). Ourwork anticipates a set of largely unexplored risks at the level of situatedinteraction, and offers practical suggestions to help LLM technologies behaveas 'good' social actors and treat people respectfully.

What makes for a 'good' social actor? Using respect as a lens to evaluate interactions with language agents