activity
20192023
most citedMarrying Fairness and Explainability in Supervised Learning

39 citations · 43 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CY2023★ 2 cited

Towards AI Transparency and Accountability: A Global Framework for Exchanging Information on AI Systems

Warren Buckley, Adrian Byrne, Nicholas Perello +4

We propose that future AI transparency and accountability regulations are based on an open global standard for exchanging information about AI systems, which allows co-existence of…

cs.LG2023★ 1 cited

Simple Steps to Success: A Method for Step-Based Counterfactual Explanations

Jenny Hamer, Nicholas Perello, Jake Valladares +2

Algorithmic recourse is a process that leverages counterfactual explanations, going beyond understanding why a system produced a given classification, to providing a user with acti…

cs.LG2022★ 39 cited

Marrying Fairness and Explainability in Supervised Learning

Przemyslaw Grabowicz, Nicholas Perello, Aarshee Mishra

Machine learning algorithms that aid human decision-making may inadvertently discriminate against certain protected groups. We formalize direct discrimination as a direct causal ef…

cs.AI2022★ 1 cited

On Optimizing Interventions in Shared Autonomy

Weihao Tan, David Koleczek, Siddhant Pradhan +7

Shared autonomy refers to approaches for enabling an autonomous agent to collaborate with a human with the aim of improving human performance. However, besides improving performanc…

cs.LG2019

Learning from Discriminatory Training Data

Przemyslaw A. Grabowicz, Nicholas Perello, Kenta Takatsu

Supervised learning systems are trained using historical data and, if the data was tainted by discrimination, they may unintentionally learn to discriminate against protected group…