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Towards Fair and Explainable Supervised Learning

Aarshee Mishra*, Nicholas Perello*, and Przemyslaw A. Grabowicz. Towards Fair and Explainable Supervised Learning. In ICML Workshop on Socially Responsible Machine Learning (SRML), 2021.

Intervention Aware Shared Autonomy

Weihao Tan*, David Koleczek*, Siddhant Pradhan*, Nicholas Perello, Vivek Chettiar, Nan Ma, Aaslesha Rajaram, Vishal Rohra, Soundar Srinivasan, H M Sajjad Hossain^, and Yash Chandak^. Intervention Aware Shared Autonomy. In ICML Workshop on Human-AI Collaboration in Sequential Decision-Making, 2021.

Resilience of Supervised Learning Algorithms to Discriminatory Poisoning of Training Data

Przemyslaw A. Grabowicz and Nicholas Perello. Resilience of Supervised Learning Algorithms to Discriminatory Poisoning of Training Data. In 7th International Conference on Computational Social Science (IC2S2), 2021.

On Optimizing Interventions in Shared Autonomy

Weihao Tan*, David Koleczek*, Siddhant Pradhan*, Nicholas Perello, Vivek Chettiar, Nan Ma, Aaslesha Rajaram, Vishal Rohra, Soundar Srinivasan, H M Sajjad Hossain^, and Yash Chandak^. On Optimizing Interventions in Shared Autonomy. In Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), 2022 https://cdn.aaai.org/ojs/20471/20471-13-24484-1-2-20220628.pdf

Marrying Fairness and Explainability in Supervised Learning

Przemyslaw A. Grabowicz, Nicholas Perello, and Aarshee Mishra. Marrying Fairness and Explainability in Supervised Learning. In ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT), 2022. https://dl.acm.org/doi/10.1145/3531146.3533236

Learning from Discriminatory Training Data

Przemyslaw A. Grabowicz*, Nicholas Perello*, and Kenta Takatsu. Learning from Discriminatory Training Data. In AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2023 https://dl.acm.org/doi/10.1145/3600211.3604710

Towards an AI Accountability Policy

Przemyslaw A. Grabowicz, Nicholas Perello, Yair Zick. Towards an AI Accountability Policy. In Comment on FR Doc 2023-07776 Posted by the National Telecommunications and Information Administration, 2023.

Simple Steps to Success: Axiomatics of Distance-Based Algorithmic Recourse

Jenny Hamer*, Nicholas Perello*, Jake Valladares, Vignesh Viswanathan*, Yair Zick. Simple Steps to Success: Axiomatics of Distance-Based Algorithmic Recourse. 2024. https://openreview.net/pdf?id=R6ey5DKaoX

Discrimination Induced by Algorithmic Recourse Objectives

Nicholas Perello, Cyrus Cousins, Yair Zick, Przemyslaw A. Grabowicz. Discrimination Induced by Algorithmic Recourse Objectives. To appear in ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT), 2025.