Optimizing fairness tradeoffs in machine learning with multiobjective meta-models

Understand

Improving the fairness of machine learning models is a nuanced task that requires decision makers to reason about multiple, conflicting criteria.

• The majority of fair machine learning methods transform the error-fairness trade-off into a single objective problem with a parameter controlling the relative importance of error versus fairness.
• We propose instead to directly optimize the error-fairness tradeoff by using multi-objective optimization.
• We present a flexible framework for defining the fair machine learning task as a weighted classification problem with multiple cost functions.