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Rahul Nair

14 accepted papers

2026

CoFrGeNet: Continued Fraction Architectures for Language Generation

Amit Dhurandhar, Vijil Chenthamarakshan, Dennis Wei, Tejaswini Pedapati, Karthikeyan Natesan Ramamurthy, Rahul Nair
ICML 2026poster

Transformers are arguably the preferred architecture for language generation. In this paper, inspired by continued fractions, we introduce a new function class for generative modeling. The architecture family implementing this function class is named CoFrGeNets - Continued Fraction Generative Networ…

Cited by 0Source ↗Scholar ↗
2026

Risk Atlas Nexus: A System for Managing AI Risks

Inge Vejsbjerg, Rahul Nair, Elizabeth M. Daly, Dhaval Salwala, Seshu Tirupathi
AAAI 2026technical

We present Risk Atlas Nexus, an open source system for governing AI risks. The system unifies several risk classification frameworks through a common ontology. Given an AI application use case (called an intent), the system estimates risks and associated mitigations that are linked to identified ris

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2025

DPA: A one-stop metric to measure bias amplification in classification datasets

Bhanu Tokas, Rahul Nair, Hannah Kerner
NeurIPS 2025poster

Most ML datasets today contain biases. When we train models on these datasets, they often not only learn these biases but can worsen them --- a phenomenon known as bias amplification. Several co-occurrence-based metrics have been proposed to measure bias amplification in classification datasets. The…

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2025

EvalAssist: LLM-as-a-Judge Simplified

Michael Desmond, Zahra Ashktorab, Werner Geyer, Elizabeth M. Daly, Martín Santillán Cooper, Qian Pan +3
AAAI 2025technical

We present EvalAssist, a framework that simplifies the LLM- as-a-judge workflow. The system provides an online criteria development environment, where users can interactively build, test, and share custom evaluation criteria in a structured and portable format. A library of LLM based evaluators is m…

Cited by 1Source ↗PDF ↗Scholar ↗
2024

Interactive Human-Centric Bias Mitigation

Inge Vejsbjerg, Elizabeth M. Daly, Rahul Nair, Svetoslav Nizhnichenkov
AAAI 2024technical

Bias mitigation algorithms differ in their definition of bias and how they go about achieving that objective. Bias mitigation algorithms impact different cohorts differently and allowing end users and data scientists to understand the impact of these differences in order to make informed choices is…

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2024

On Efficient and Statistical Quality Estimation for Data Annotation

Jan-Christoph Klie, Juan Haladjian, Marc Kirchner, Rahul Nair
ACL 2024long

Annotated datasets are an essential ingredient to train, evaluate, compare and productionalize supervised machine learning models. It is therefore imperative that annotations are of high quality. For their creation, good quality management and thereby reliable quality estimates are needed. Then, if…

Cited by 2Source ↗PDF ↗Scholar ↗
2024

Ranking Large Language Models without Ground Truth

Amit Dhurandhar, Rahul Nair, Moninder Singh, Elizabeth Daly, Karthikeyan Natesan Ramamurthy
ACL 2024findings

Evaluation and ranking of large language models (LLMs) has become an important problem with the proliferation of these models and their impact. Evaluation methods either require human responses which are expensive to acquire or use pairs of LLMs to evaluate each other which can be unreliable. In thi…

Cited by 3Source ↗PDF ↗Scholar ↗
2023

An Explainable Forecasting System for Humanitarian Needs Assessment

Rahul Nair, Bo Madsen, Alexander Kjærum
AAAI 2023technical

We present a machine learning system for forecasting forced displacement populations deployed at the Danish Refugee Council (DRC). The system, named Foresight, supports long term forecasts aimed at humanitarian response planning. It is explainable, providing evidence and context supporting the forec…

Cited by 2Source ↗PDF ↗code ↗Scholar ↗
2023

Cookie Consent Has Disparate Impact on Estimation Accuracy

Erik Miehling, Rahul Nair, Elizabeth M. Daly, Karthikeyan Natesan Ramamurthy, Robert Nelson Redmond
NeurIPS 2023poster

Cookies are designed to enable more accurate identification and tracking of user behavior, in turn allowing for more personalized ads and better performing ad campaigns. Given the additional information that is recorded, questions related to privacy and fairness naturally arise. How does a user's co…

Cited by 1Source ↗PDF ↗Scholar ↗
2023

Interpretable differencing of machine learning models

Swagatam Haldar, Diptikalyan Saha, Dennis Wei, Rahul Nair, Elizabeth M. Daly
UAI 2023poster

Understanding the differences between machine learning (ML) models is of interest in scenarios ranging from choosing amongst a set of competing models, to updating a deployed model with new training data. In these cases, we wish to go beyond differences in overall metrics such as accuracy to identif…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2022

On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach

Dennis Wei, Rahul Nair, Amit Dhurandhar, Kush R. Varshney, Elizabeth M. Daly, Moninder Singh
NeurIPS 2022accept

Interpretable and explainable machine learning has seen a recent surge of interest. We focus on safety as a key motivation behind the surge and make the relationship between interpretability and safety more quantitative. Toward assessing safety, we introduce the concept of *maximum deviation* via an…

Cited by 9Source ↗PDF ↗Scholar ↗
2021

User Driven Model Adjustment via Boolean Rule Explanations

Elizabeth M. Daly, Massimiliano Mattetti, Öznur Alkan, Rahul Nair
AAAI 2021technical

AI solutions are heavily dependant on the quality and accuracy of the input training data, however the training data may not always fully reflect the most up-to-date policy landscape or may be missing business logic. The advances in explainability have opened the possibility of allowing users to int…

Cited by 12Source ↗PDF ↗code ↗Scholar ↗
2021

What Changed? Interpretable Model Comparison

Rahul Nair, Massimiliano Mattetti, Elizabeth Daly, Dennis Wei, Oznur Alkan, Yunfeng Zhang
IJCAI 2021poster

We consider the problem of distinguishing two machine learning (ML) models built for the same task in a human-interpretable way. As models can fail or succeed in different ways, classical accuracy metrics may mask crucial qualitative differences. This problem arises in a few contexts. In business ap…

Cited by 12Source ↗PDF ↗code ↗Scholar ↗