17 citations · 42 across the 15 of their papers we have counts for
4 papers · 1 filter
BiMi Sheets: Infosheets for bias mitigation methods
MaryBeth Defrance, Guillaume Bied, Maarten Buyl +2
Over the past 15 years, hundreds of bias mitigation methods have been proposed in the pursuit of fairness in machine learning (ML). However, algorithmic biases are domain-, task-,…
ABCFair: an Adaptable Benchmark approach for Comparing Fairness Methods
MaryBeth Defrance, Maarten Buyl, Tijl De Bie
Numerous methods have been implemented that pursue fairness with respect to sensitive features by mitigating biases in machine learning. Yet, the problem settings that each method…
fairret: a Framework for Differentiable Fairness Regularization Terms
Maarten Buyl, MaryBeth Defrance, Tijl De Bie
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 KL-Divergence between a Graph Model and its Fair I-Projection as a Fairness Regularizer
Maarten Buyl, Tijl De Bie
Learning and reasoning over graphs is increasingly done by means of probabilistic models, e.g. exponential random graph models, graph embedding models, and graph neural networks. W…