"I followed what felt right, not what I was told": Autonomy, Coaching, and Recognizing Bias Through AI-Mediated Dialogue

TL;DR

This study investigates how AI-mediated dialogue can help people recognize ableist microaggressions, showing that dialogue-based interventions improve bias recognition but also introduce complex emotional and perceptual trade-offs.

Contribution

The paper introduces a validated vignette corpus, an AI intervention platform, and insights into the design trade-offs of bias nudging in conversational systems.

Findings

Dialogue-based conditions enhanced bias recognition over reading.

Biased nudges increased negativity and differentiation of bias.

Inclusive nudges were more accepted and balanced.

Abstract

Ableist microaggressions remain pervasive in everyday interactions, yet interventions to help people recognize them are limited. We present an experiment testing how AI-mediated dialogue influences recognition of ableism. 160 participants completed a pre-test, intervention, and a post-test across four conditions: AI nudges toward bias (Bias-Directed), inclusion (Neutral-Directed), unguided dialogue (Self-Directed), and a text-only non-dialogue (Reading). Participants rated scenarios on standardness of social experience and emotional impact; those in dialogue-based conditions also provided qualitative reflections. Quantitative results showed dialogue-based conditions produced stronger recognition than Reading, though trajectories diverged: biased nudges improved differentiation of bias from neutrality but increased overall negativity. Inclusive or no nudges remained more balanced, while Reading participants showed weaker gains and even declines. Qualitative findings revealed biased nudges were often rejected, while inclusive nudges were adopted as scaffolding. We contribute a validated vignette corpus, an AI-mediated intervention platform, and design implications highlighting trade-offs conversational systems face when integrating bias-related nudges.