6 citations · 10 across the 6 of their papers we have counts for
3 papers · 1 filter
Fairness under uncertainty in sequential decisions
Michelle Seng Ah Lee, Kirtan Padh, David Watson +2
Fair machine learning (ML) methods help identify and mitigate the risk that algorithms encode or automate social injustices. Algorithmic approaches alone cannot resolve structural…
Cluster-Dags as Powerful Background Knowledge For Causal Discovery
Jan Marco Ruiz de Vargas, Kirtan Padh, Niki Kilbertus
Finding cause-effect relationships is of key importance in science. Causal discovery aims to recover a graph from data that succinctly describes these cause-effect relationships. H…
Addressing Fairness in Classification with a Model-Agnostic Multi-Objective Algorithm
Kirtan Padh, Diego Antognini, Emma Lejal Glaude +2
The goal of fairness in classification is to learn a classifier that does not discriminate against groups of individuals based on sensitive attributes, such as race and gender. One…