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Found 5 papers, 5 papers with code
fairret: a Framework for Differentiable Fairness Regularization Terms
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.
RankFormer: Listwise Learning-to-Rank Using Listwide Labels
Web applications where users are presented with a limited selection of items have long employed ranking models to put the most relevant results first.
Optimal Transport of Classifiers to Fairness
In past work on fairness in machine learning, the focus has been on forcing the prediction of classifiers to have similar statistical properties for people of different demographics.
The KL-Divergence between a Graph Model and its Fair I-Projection as a Fairness Regularizer
Given this, we propose a fairness regularizer defined as the KL-divergence between the graph model and its I-projection onto the set of fair models.
DeBayes: a Bayesian Method for Debiasing Network Embeddings
As machine learning algorithms are increasingly deployed for high-impact automated decision making, ethical and increasingly also legal standards demand that they treat all individuals fairly, without discrimination based on their age, gender, race or other sensitive traits.
