fairret: a Framework for Differentiable Fairness Regularization Terms
Maarten Buyl, MaryBeth Defrance, Tijl De Bie
TL;DR
Fairret is a flexible framework that introduces differentiable fairness regularization terms, enabling seamless integration of fairness constraints into machine learning models using automatic differentiation, demonstrated with a PyTorch implementation.
Contribution
We propose a modular, differentiable framework for fairness regularization terms that can be efficiently integrated into modern machine learning pipelines.
Findings
Fairret can compute a wide class of fairness metrics efficiently.
Gradients of fairrets effectively enforce fairness with minimal impact on accuracy.
The PyTorch implementation facilitates easy adoption in existing workflows.
Abstract
Current fairness toolkits in machine learning only admit a limited range of fairness definitions and have seen little integration with automatic differentiation libraries, despite the central role these libraries play in modern machine learning pipelines. We introduce a framework of fairness regularization terms (fairrets) which quantify bias as modular, flexible objectives that are easily integrated in automatic differentiation pipelines. By employing a general definition of fairness in terms of linear-fractional statistics, a wide class of fairrets can be computed efficiently. Experiments show the behavior of their gradients and their utility in enforcing fairness with minimal loss of predictive power compared to baselines. Our contribution includes a PyTorch implementation of the fairret framework.
Peer Reviews
Decision·ICLR 2024 poster
- The presented SmoothMax regularization terms are elegant and provide expressive representations for widely applied group fairness metrics. - The methods can be combined with automatic differentiation tools, such as PyTorch. - The methods can be naturally applied with multiple axes of sensitive attributes, allowing wider applications.
- The method still applies relaxed fairness metrics, rather than the exact metrics as the regularization terms. - A superior learning objective should be a minimax game with the optimization of the $\lambda$-player. As far as my understanding, the authors use fixed $\lambda$ values as a hyper-parameter and run grid search to get the optimal results.
I think the authors propose a valuable contribution to the fairness community. Specifically, I appreciate the effort the authors make on combining multiple fairness notions into a general framework. I also think that providing a python package can be valuable for research and adaptation of fairness in ML.
The theoretical contribution of this work is in my opinion quite limited. While combining different fairness notions is valuable, I do not think that this is in itself a theoretical contribution. For instance, if in (3) you fix $\overline{\gamma}(h)=c$, then a norm based regularizer would be convex in $f(X)$. Thus if the model is linear, you would get a convex regularizer. Combine this with a convex $\mathcal{L}_Y$ and your problem is convex in the model parameters. This would allow you to get f
+ The paper provides a good formal coverage of the fundamental concepts.
1. I have concerns about the novelty. It appears that the paper's novelty is a formal fairness regularization framework. The paper criticizes existing frameworks for not being formal and limited in terms of fairness definitions. However, the paper does not showcase what the benefit of this formal framework is. Moreover, it is not clear why FFB or FairTorch cannot be extended to include more fairness definitions. 2. I find "fairness tools" misleading. This is sometimes used to refer to fairness
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Ethics and Social Impacts of AI · Adversarial Robustness in Machine Learning