Should agentic conversational AI change how we think about ethics? Characterising an interactional ethics centred on respect

TL;DR

This paper advocates for an interactional ethics framework for agentic conversational AI, emphasizing respect and pragmatic social factors over traditional output-focused criteria, to ensure ethical behavior in social interactions.

Contribution

It introduces an interactional approach to AI ethics that considers relational and situational factors, addressing overlooked social interaction risks in agentic LLM systems.

Findings

Highlights importance of pragmatics in ethical AI interactions

Proposes practical guidelines for respectful AI behavior

Identifies new social risks in agentic AI interactions

Abstract

With the growing popularity of conversational agents based on large language models (LLMs), we need to ensure their behaviour is ethical and appropriate. Work in this area largely centres around the 'HHH' criteria: making outputs more helpful and honest, and avoiding harmful (biased, toxic, or inaccurate) statements. Whilst this semantic focus is useful when viewing LLM agents as mere mediums or output-generating systems, it fails to account for pragmatic factors that can make the same speech act seem more or less tactless or inconsiderate in different social situations. With the push towards agentic AI, wherein systems become increasingly proactive in chasing goals and performing actions in the world, considering the pragmatics of interaction becomes essential. We propose an interactional approach to ethics that is centred on relational and situational factors. We explore 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 social interaction, and offers practical suggestions to help agentic LLM technologies treat people well.