Designing Tools for Semi-Automated Detection of Machine Learning Biases: An Interview Study
This work addresses the need for effective bias detection tools to mitigate financial and ethical risks in ML, but it is incremental as it focuses on design insights rather than a new method.
The study investigated the design considerations for semi-automated tools to detect biases in machine learning models by interviewing 11 practitioners, highlighting four key considerations to guide future tool development.
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.