  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 detection tools. Based
on the findings, we highlight four considerations in designing to guide system
designers who aim to create future tools for bias detection.
