Mutual information-based group explainers with coalition structure for machine learning model explanations. - Alexey Miroshnikov; Konstandinos Kotsiopoulos; Arjun Ravi Kannan (CoRR, 2021)

Model-agnostic bias mitigation methods with regressor distribution control for Wasserstein-based fairness metrics. - Alexey Miroshnikov; Konstandinos Kotsiopoulos; Ryan Franks; Arjun Ravi Kannan (CoRR, 2021)

A United States Fair Lending Perspective on Machine Learning. - Patrick Hall; Benjamin Cox; Steven Dickerson; Arjun Ravi Kannan; Raghu Kulkarni; Nicholas Schmidt (Frontiers Artif. Intell., 2021)

Wasserstein-based fairness interpretability framework for machine learning models. - Alexey Miroshnikov; Konstandinos Kotsiopoulos; Ryan Franks; Arjun Ravi Kannan (Mach. Learn., 2022)

On marginal feature attributions of tree-based models. - Khashayar Filom; Alexey Miroshnikov; Konstandinos Kotsiopoulos; Arjun Ravi Kannan (CoRR, 2023)

Approximation of group explainers with coalition structure using Monte Carlo sampling on the product space of coalitions and features. - Konstandinos Kotsiopoulos; Alexey Miroshnikov; Khashayar Filom; Arjun Ravi Kannan (CoRR, 2023)

Mechanistic interpretability of large language models with applications to the financial services industry. - Ashkan Golgoon; Khashayar Filom; Arjun Ravi Kannan (ICAIF, 2024)

MS-IMAP - A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learning. - Shay Deutsch; Lionel Yelibi; Alex Tong Lin; Arjun Ravi Kannan (CoRR, 2024)

MBExplainer: Multilevel bandit-based explanations for downstream models with augmented graph embeddings. - Ashkan Golgoon; Ryan Franks; Khashayar Filom; Arjun Ravi Kannan (CoRR, 2024)

Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews. - Izunna Okpala; Ashkan Golgoon; Arjun Ravi Kannan (CoRR, 2025)