Large language models (LLMs) trained on real-world data can inadvertently reflect harmful
societal biases, particularly toward historically marginalized communities. While previous
work has primarily focused on harms related to age and race, emerging research has shown
that biases toward disabled communities exist. This study extends prior work exploring the
existence of harms by identifying categories of LLM-perpetuated harms toward the disability
community. We conducted 19 focus groups, during which 56 participants with disabilities …