RSM: An Explainable Predictive Sales Route Selector. - Bei Chen; Rahul Nair 004; Inge Vejsbjerg (ICDM Workshops, 2019)

Scenario-based XAI for Humanitarian Aid Forecasting. - Josh Andres; Christine T. Wolf; Sergio Cabrero Barros; Erick Oduor; Rahul Nair 004; Alexander Kjærum; Anders Bech Tharsgaard; Bo Schwartz Madsen (CHI Extended Abstracts, 2020)

A machine learning approach to scenario analysis and forecasting of mixed migration. - Rahul Nair 004; Bo Schwartz Madsen; Helena Lassen; Serge Baduk; Srividya Nagarajan; Lars Henrik Mogensen; Rana Novack; Rebecca Curzon; Jurij Paraszczak; Sanne Urbak (IBM J. Res. Dev., 2020)

User Driven Model Adjustment via Boolean Rule Explanations. - Elizabeth M. Daly; Massimiliano Mattetti; Öznur Alkan; Rahul Nair 004 (AAAI, 2021)

What Changed? Interpretable Model Comparison. - Rahul Nair 004; Massimiliano Mattetti; Elizabeth Daly; Dennis Wei; Oznur Alkan; Yunfeng Zhang (IJCAI, 2021)

AIMEE: Interactive model maintenance with rule-based surrogates. - Owen Cornec; Rahul Nair 004; Elizabeth Daly; Oznur Alkan; Dennis Wei (NeurIPS (Competition and Demos), 2021)

Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents. - Jasmina Gajcin; Rahul Nair 004; Tejaswini Pedapati; Radu Marinescu 002; Elizabeth Daly; Ivana Dusparic (CoRR, 2021)

FROTE: Feedback Rule-Driven Oversampling for Editing Models. - Oznur Alkan; Dennis Wei; Massimiliano Mattetti; Rahul Nair 004; Elizabeth Daly; Diptikalyan Saha (MLSys, 2022)

On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach. - Dennis Wei; Rahul Nair 004; Amit Dhurandhar; Kush R. Varshney; Elizabeth Daly; Moninder Singh (NeurIPS, 2022)

Boolean Decision Rules for Reinforcement Learning Policy Summarisation. - James McCarthy; Rahul Nair 004; Elizabeth Daly; Radu Marinescu 002; Ivana Dusparic (CoRR, 2022)

An Explainable Forecasting System for Humanitarian Needs Assessment. - Rahul Nair 004; Bo Schwartz Madsen; Alexander Kjærum (AAAI, 2023)

Iterative Reward Shaping Using Human Feedback for Correcting Reward Misspecification. - Jasmina Gajcin; James McCarthy; Rahul Nair 004; Radu Marinescu 002; Elizabeth Daly; Ivana Dusparic (ECAI, 2023)

AutoDOViz: Human-Centered Automation for Decision Optimization. - Daniel Karl I. Weidele; Shazia Afzal; Abel N. Valente; Cole Makuch; Owen Cornec; Long Vu; Dharmashankar Subramanian; Werner Geyer; Rahul Nair 004; Inge Vejsbjerg; Radu Marinescu 002; Paulito P. Palmes; et al. (IUI, 2023)

Cookie Consent Has Disparate Impact on Estimation Accuracy. - Erik Miehling; Rahul Nair 004; Elizabeth Daly; Karthikeyan Natesan Ramamurthy; Robert Redmond (NeurIPS, 2023)

Interpretable differencing of machine learning models. - Swagatam Haldar; Diptikalyan Saha; Dennis Wei; Rahul Nair 004; Elizabeth M. Daly (UAI, 2023)

Explaining Knock-on Effects of Bias Mitigation. - Svetoslav Nizhnichenkov; Rahul Nair 004; Elizabeth Daly; Brian Mac Namee (CoRR, 2023)

Interactive Human-Centric Bias Mitigation. - Inge Vejsbjerg; Elizabeth M. Daly; Rahul Nair 004; Svetoslav Nizhnichenkov (AAAI, 2024)

Ranking Large Language Models without Ground Truth. - Amit Dhurandhar; Rahul Nair 004; Moninder Singh; Elizabeth Daly; Karthikeyan Natesan Ramamurthy (ACL (Findings), 2024)

Human-Centered Design Recommendations for LLM-as-a-Judge. - Qian Pan; Zahra Ashktorab; Michael Desmond; Martin Santillan Cooper; James M. Johnson; Rahul Nair 004; Elizabeth Daly; Werner Geyer (CoRR, 2024)

Aligning Human and LLM Judgments: Insights from EvalAssist on Task-Specific Evaluations and AI-assisted Assessment Strategy Preferences. - Zahra Ashktorab; Michael Desmond; Qian Pan; James M. Johnson; Martin Santillan Cooper; Elizabeth M. Daly; Rahul Nair 004; Tejaswini Pedapati; Swapnaja Achintalwar; Werner Geyer (CoRR, 2024)

Black-box Uncertainty Quantification Method for LLM-as-a-Judge. - Nico Wagner; Michael Desmond; Rahul Nair 004; Zahra Ashktorab; Elizabeth M. Daly; Qian Pan; Martin Santillan Cooper; James M. Johnson; Werner Geyer (CoRR, 2024)

EvalAssist: LLM-as-a-Judge Simplified. - Michael Desmond; Zahra Ashktorab; Werner Geyer; Elizabeth M. Daly; Martín Santillán Cooper; Qian Pan; Rahul Nair 004; Nico Wagner; Tejaswini Pedapati (AAAI, 2025)

Humble AI in the real-world: the case of algorithmic hiring. - Rahul Nair 004; Inge Vejsbjerg; Elizabeth M. Daly; Christos Varytimidis; Bran Knowles (CHIWORK (Adjunct), 2025)

A Representation-Level Assessment of Bias Mitigation in Foundation Models. - Svetoslav Nizhnichenkov; Rahul Nair 004; Elizabeth Daly; Brian Mac Namee (PKDD/ECML Workshops (1), 2025)

EvalAssist: Insights on Task-Specific Evaluations and AI-Assisted Judgment Strategy Preferences. - Zahra Ashktorab; Michael Desmond; Qian Pan; James M. Johnson; Martín Santillán Cooper; Elizabeth M. Daly; Rahul Nair 004; Tejaswini Pedapati; Hyo Jin Do; Werner Geyer (UIST, 2025)

Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic Hiring. - Buse Sibel Korkmaz; Rahul Nair 004; Elizabeth M. Daly; Evangelos Anagnostopoulos; Christos Varytimidis; Antonio del Rio-Chanona (CoRR, 2025)

AI Risk Atlas: Taxonomy and Tooling for Navigating AI Risks and Resources. - Frank Bagehorn; Kristina Brimijoin; Elizabeth M. Daly; Jessica He; Michael Hind; Luis Garcés-Erice; Christopher Giblin; Ioana Giurgiu; Jacquelyn Martino; Rahul Nair 004; David Piorkowski; Ambrish Rawat; et al. (CoRR, 2025)

Paying Alignment Tax with Contrastive Learning. - Buse Sibel Korkmaz; Rahul Nair 004; Elizabeth M. Daly; Antonio del Rio-Chanona (CoRR, 2025)

Interpreting LLM-as-a-Judge Policies via Verifiable Global Explanations. - Jasmina Gajcin; Erik Miehling; Rahul Nair 004; Elizabeth Daly; Radu Marinescu 002; Seshu Tirupathi (CoRR, 2025)

Risk Atlas Nexus: A System for Managing AI Risks. - Inge Vejsbjerg; Rahul Nair 004; Elizabeth M. Daly; Dhaval Salwala; Seshu Tirupathi (AAAI, 2026)

CoFrGeNet: Continued Fraction Architectures for Language Generation. - Amit Dhurandhar; Vijil Chenthamarakshan; Dennis Wei; Tejaswini Pedapati; Karthikeyan Natesan Ramamurthy; Rahul Nair 004 (CoRR, 2026)