The study fine-tunes a language model to automate the extraction of gender and race references in stories generated by artificial intelligence, addressing biases such as poor performance with non-binary pronouns. The approach improves accuracy, achieving over 98% for gender and name identification, and addresses the challenges of modeling minority identities.
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