Shiny Stories, Hidden Struggles: Investigating the Representation of Disability Through the Lens of LLMs
arXiv:2605.20191v1 Announce Type: new Abstract: Modern Large Language Models (LLMs) have recently attracted much attention for their ability to simulate human behavior and generate text that reflects personas and demographic groups. While these capabilities can open up a multitude of diverse applications across fields, it is crucial to examine how such models represent various target groups since LLMs can perpetuate and amplify biases or discrimination against historically marginalized communities or, alternatively, as a result of debiasing efforts, overcorrect by portraying overly positive st
The rapid advancement and widespread deployment of LLMs necessitate a critical examination of their societal impact, particularly concerning vulnerable demographics, as their influence grows.
Sophisticated readers should care because the inherent biases within LLMs can perpetuate discrimination or, conversely, lead to overcorrection, impacting public perception and policy towards marginalized groups.
Understanding LLM biases regarding disability shifts focus from mere technical capability to ethical deployment, influencing development, regulation, and societal integration of AI.
- · AI ethics researchers
- · Disability advocacy groups
- · Responsible AI developers
- · Regulators
- · Developers ignoring bias
- · Unregulated AI platforms
- · Generative AI users
Research identifies and quantifies the specific biases related to disability within LLMs.
This understanding leads to the development of new debiasing techniques and ethical guidelines for AI model training and deployment.
Future AI systems are designed with inherent safeguards against perpetuating harmful stereotypes, fostering more inclusive digital environments for all users.
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Read at arXiv cs.CL