Mitigating Unfair Bias in ML Models with the MinDiff Framework

Description

Summary

• One broad category for applying ML responsibly is the task of classification — systems that sort data into labeled categories.
• Unfair Biases in Classifiers To illustrate how MinDiff can be used, consider an example of a product policy classifier that is tasked with identifying and removing text comments that could be considered toxic.
• One of the most common metrics is equality of opportunity, which, in our example, means measuring and seeking to minimize the difference in false positive rate (FPR) across groups.
• Because any decrease in accuracy caused by the mitigation approach could result in the moderation model allowing more toxic comments, striking the right balance is crucial.