Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data

TL;DR

This paper introduces Fairness Warnings and Fair-MAML, two algorithms designed to promote fairness in machine learning models with minimal data, enabling quick adaptation and interpretability of fairness boundaries.

Contribution

The paper presents novel algorithms for interpretable fairness warnings and fair meta-learning, addressing the challenge of training fair models with limited data and providing the first exploration of K-shot fairness.

Findings

Fairness Warnings offer interpretable boundary conditions for fairness.

Fair-MAML enables rapid fair model adaptation with few data points.

Combined approach improves fairness and efficiency in new tasks.

Abstract

Motivated by concerns surrounding the fairness effects of sharing and transferring fair machine learning tools, we propose two algorithms: Fairness Warnings and Fair-MAML. The first is a model-agnostic algorithm that provides interpretable boundary conditions for when a fairly trained model may not behave fairly on similar but slightly different tasks within a given domain. The second is a fair meta-learning approach to train models that can be quickly fine-tuned to specific tasks from only a few number of sample instances while balancing fairness and accuracy. We demonstrate experimentally the individual utility of each model using relevant baselines and provide the first experiment to our knowledge of K-shot fairness, i.e. training a fair model on a new task with only K data points. Then, we illustrate the usefulness of both algorithms as a combined method for training models from a few data points on new tasks while using Fairness Warnings as interpretable boundary conditions under which the newly trained model may not be fair.