Identifying Implicit Bias in LLM-based Chat AI Toward People with Intellectual Disabilities
Background: This work investigates the presence of implicit bias in Large Language Model (LLM)-based chat AI models directed toward people with intellectual disabilities (ID). Objective: The study aims to identify and measure representational differences related to people with ID and examine them to identify implicit biases inherent in AI chat generation technologies. Methods: Utilizing the GPT-4-Turbo model, we requested story-generation based on 10 prompt stems with and without descriptors for ID. This process was repeated using four other LLMs (OpenAI GPT-4o, Meta Llama-3-3-70B-Instruct, Anthropic Claude-3-5-Sonnet, and Mistral-Large-2411). The resulting 25,000 computer-generated stori...
| Authors | Karly V. Coffey, Gloria L. Krahn, John P. Hanley et al. |
|---|---|
| Primary category | cs.CY |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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