Identifying Fairness Issues in Automatically Generated Testing Content

arXiv:2404.15104v1 Announce Type: new
Abstract: Natural language generation tools are powerful and effective for generating content. However, language models are known to display bias and fairness issues, making them impractical to deploy for many use cases. We here focus on how fairness issues impact automatically generated test content, which can have stringent requirements to ensure the test measures only what it was intended to measure. Specifically, we identify test content that is focused on particular domains and experiences that only …

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