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    Open Access

    Separating facts and evaluation: motivation, account, and learnings from a novel approach to evaluating the human impacts of machine learning

    In this paper, we outline a new method for evaluating the human impact of machine-learning (ML) applications. In partnership with Underwriters Laboratories Inc., we have developed a framework to evaluate the i...

    Ryan Jenkins, Kristian Hammond, Sarah Spurlock, Leilani Gilpin in AI & SOCIETY (2023)

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