A Disability Lens towards Biases in GPT-3 Generated Open-Ended Languages
This work addresses fairness concerns in language models for disabled users, but it is incremental as it focuses on measuring biases without introducing new methods.
The study measured biases in GPT-3 generated text from a disability perspective, identifying specific biases that risk product usability and fairness.
Language models (LM) are becoming prevalent in many language-based application spaces globally. Although these LMs are improving our day-to-day interactions with digital products, concerns remain whether open-ended languages or text generated from these models reveal any biases toward a specific group of people, thereby risking the usability of a certain product. There is a need to identify whether these models possess bias to improve the fairness in these models. This gap motivates our ongoing work, where we measured the two aspects of bias in GPT-3 generated text through a disability lens.