What makes for a 'good' social actor? Using respect as a lens to
  evaluate interactions with language agents
Lize Alberts Geoff Keeling Amanda McCroskery
With the growing popularity of dialogue agents based on large language models
(LLMs), urgent attention has been drawn to finding ways to ensure their
behaviour is ethical and appropriate. These are largely interpreted in terms of
the 'HHH' criteria: making outputs more helpful and honest, and avoiding
harmful (biased, toxic, or inaccurate) statements. Whilst this semantic focus
is useful from the perspective of viewing LLM agents as mere mediums for
information, it fails to account for pragmatic factors that can make the same
utterance seem more or less offensive or tactless in different social
situations. We propose an approach to ethics that is more centred on relational
and situational factors, exploring what it means for a system, as a social
actor, to treat an individual respectfully in a (series of) interaction(s). Our
work anticipates a set of largely unexplored risks at the level of situated
interaction, and offers practical suggestions to help LLM technologies behave
as 'good' social actors and treat people respectfully.