65 citations · 287 across the 12 of their papers we have counts for
3 papers · 1 filter
Metrics and methods for a systematic comparison of fairness-aware machine learning algorithms
Gareth P. Jones, James M. Hickey, Pietro G. Di Stefano +3
Understanding and removing bias from the decisions made by machine learning models is essential to avoid discrimination against unprivileged groups. Despite recent progress in algo…
Fairness by Explicability and Adversarial SHAP Learning
James M. Hickey, Pietro G. Di Stefano, Vlasios Vasileiou
The ability to understand and trust the fairness of model predictions, particularly when considering the outcomes of unprivileged groups, is critical to the deployment and adoption…
Counterfactual fairness: removing direct effects through regularization
Pietro G. Di Stefano, James M. Hickey, Vlasios Vasileiou
Building machine learning models that are fair with respect to an unprivileged group is a topical problem. Modern fairness-aware algorithms often ignore causal effects and enforce…