Making Fair ML Software using Trustworthy Explanation
This work addresses bias in ML software used in high-impact domains like finance and hiring, but it is incremental as it builds on existing explanation methods.
The paper tackles the problem of bias in machine learning software by identifying shortcomings in current bias measures and explanation methods, and proposes a K nearest neighbors-based method that provides more trustworthy results for practitioners.
Machine learning software is being used in many applications (finance, hiring, admissions, criminal justice) having a huge social impact. But sometimes the behavior of this software is biased and it shows discrimination based on some sensitive attributes such as sex, race, etc. Prior works concentrated on finding and mitigating bias in ML models. A recent trend is using instance-based model-agnostic explanation methods such as LIME to find out bias in the model prediction. Our work concentrates on finding shortcomings of current bias measures and explanation methods. We show how our proposed method based on K nearest neighbors can overcome those shortcomings and find the underlying bias of black-box models. Our results are more trustworthy and helpful for the practitioners. Finally, We describe our future framework combining explanation and planning to build fair software.