4 papers
Counterfactuals for the Future
Lucius E. J. Bynum, Joshua R. Loftus, Julia Stoyanovich
Counterfactuals are often described as 'retrospective,' focusing on hypothetical alternatives to a realized past. This description relates to an often implicit assumption about the…
Causal intersectionality for fair ranking
Ke Yang, Joshua R. Loftus, Julia Stoyanovich
In this paper we propose a causal modeling approach to intersectional fairness, and a flexible, task-specific method for computing intersectionally fair rankings. Rankings are used…
Causal Interventions for Fairness
Matt J. Kusner, Chris Russell, Joshua R. Loftus +1
Most approaches in algorithmic fairness constrain machine learning methods so the resulting predictions satisfy one of several intuitive notions of fairness. While this may help pr…
Causal Reasoning for Algorithmic Fairness
Joshua R. Loftus, Chris Russell, Matt J. Kusner +1
In this work, we argue for the importance of causal reasoning in creating fair algorithms for decision making. We give a review of existing approaches to fairness, describe work in…