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Related Experiment Video

Updated: May 31, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
07:14

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Identifying implicit bias in LLM-based chat AI toward people with intellectual disabilities.

Karly V Coffey1, Gloria L Krahn2, John P Hanley1

  • 1Special Olympics International, USA.

Disability and Health Journal
|May 28, 2026
PubMed
Summary

Large Language Models (LLMs) show implicit bias against people with intellectual disabilities (ID), portraying them negatively. This highlights the need for bias mitigation in AI development to prevent societal harm.

Keywords:
AbleismArtificial intelligenceImplicit biasIntellectual disabilitiesLarge language models

Related Experiment Videos

Last Updated: May 31, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
07:14

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Disability Studies

Background:

  • Investigates implicit bias in Large Language Model (LLM)-based chat AI.
  • Focuses on bias directed toward people with intellectual disabilities (ID).

Purpose of the Study:

  • Identify and measure representational differences for people with ID.
  • Examine AI chat generation technologies for inherent implicit biases.

Main Methods:

  • Used GPT-4-Turbo for story generation with and without ID descriptors.
  • Repeated with five other LLMs, analyzing 25,000 stories.
  • Analyzed stories for representational differences linked to bias.

Main Results:

  • Found representational differences between stories with and without ID descriptors.
  • Identified negative implicit biases, including infantilization and paternalism.
  • Observed themes of dependence, need for help, and negative perceptions.

Conclusions:

  • Implicit biases echo past discrimination against people with ID.
  • Stresses the need for diligence in AI development to counter bias.
  • Underscores importance of assessing and mitigating bias in AI to prevent harm.