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Machine learning models often make predictions that bias against certainsubgroups of input data. When undetected, machine learning biases canconstitute significant financial and ethical implications. Semi-automated toolsthat involve humans in the loop could facilitate bias detection. Yet, little isknown about the considerations involved in their design. In this paper, wereport on an interview study with 11 machine learning practitioners forinvestigating the needs surrounding semi-automated bias detection tools. Basedon the findings, we highlight four considerations in designing to guide systemdesigners who aim to create future tools for bias detection.

Designing Tools for Semi-Automated Detection of Machine Learning Biases: An Interview Study