DOI: 10.31219/osf.io/uvjqh
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Po-Ming Law, Sana Malik, Fan Du, Moumita Sinha

Abstract: 1 Machine learning models often make predictions that bias against certain subgroups of input data. When undetected, machine learning biases can constitute significant financial and ethical implications. Semi-automated tools that involve humans in the loop could facilitate bias detection. Yet, little is known about the considerations involved in their design. In this paper, we report on an interview study with 11 machine learning practitioners for investigating the needs surrounding semi-automated bias detect…

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