Exponentiated Gradient Reduction
Description
An in-processing fairness technique based on Agarwal et al.'s reductions approach that transforms fair classification into a sequence of cost-sensitive classification problems. The method uses an exponentiated gradient algorithm to iteratively reweight training data, returning a randomised classifier that achieves the lowest empirical error whilst satisfying fairness constraints. This reduction-based framework provides theoretical guarantees about both accuracy and constraint violation, making it suitable for various fairness criteria including demographic parity and equalised odds.
Example Use Cases
Fairness
Training a hiring algorithm with demographic parity constraints to ensure equal selection rates across gender groups, using iterative reweighting to balance fairness and predictive accuracy whilst maintaining legal compliance.
Transparency
Developing a loan approval model with equalised odds constraints, providing transparent documentation of the theoretical guarantees about both error rates and fairness constraint violations achieved by the reduction approach.
Reliability
Creating a medical diagnosis classifier that maintains reliable performance across demographic groups by using randomised prediction averaging, ensuring consistent healthcare delivery whilst monitoring constraint satisfaction over time.
Limitations
- Requires convex base learners for theoretical guarantees about convergence and optimality, limiting the choice of underlying models.
- Produces randomised classifiers that may give different predictions for identical inputs, which can be problematic in applications requiring consistent decisions.
- Convergence can be slow and sensitive to hyperparameter choices, particularly the learning rate and tolerance settings.
- Involves iterative retraining with adjusted weights, which can be computationally expensive for large datasets or complex models.
- Fairness constraints may significantly reduce model accuracy, and the trade-off between fairness and performance is not always transparent to practitioners.
Resources
Research Papers
A Reductions Approach to Fair Classification
We present a systematic approach for achieving fairness in a binary classification setting. While we focus on two well-known quantitative definitions of fairness, our approach encompasses many other previously studied definitions as special cases. The key idea is to reduce fair classification to a sequence of cost-sensitive classification problems, whose solutions yield a randomized classifier with the lowest (empirical) error subject to the desired constraints. We introduce two reductions that work for any representation of the cost-sensitive classifier and compare favorably to prior baselines on a variety of data sets, while overcoming several of their disadvantages.