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

2026-03-11

AI Summary

This study addresses the pervasive yet under-addressed issue of ability-based microaggressions in everyday interactions, for which effective interventions to support bias recognition remain scarce. The authors develop an empirically validated corpus of bias-laden scenarios and an AI-mediated dialogue intervention platform, then conduct a controlled experiment comparing four conditions: bias-focused prompting, inclusivity-focused prompting, unguided dialogue, and passive text reading. Findings indicate that dialogic interventions significantly outperform passive reading in enhancing bias recognition. Both inclusivity-oriented and unguided strategies effectively improve recognition while preserving emotional equilibrium, whereas bias-focused prompts, though increasing discernment, often provoke negative reactions and are frequently rejected by users. The work reveals a critical design tension between intervention efficacy and user acceptance in AI-mediated bias awareness systems, offering a novel paradigm for developing more effective and user-sensitive tools.

Technology Category

• Humans and AI: Interaction Techniques and Devices
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

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.

Problem

• ableist microaggressions
• bias recognition
• AI-mediated dialogue
• intervention

Innovation

• AI-mediated dialogue
• bias recognition
• ableism intervention
• conversational AI
• nudge design