Deborah D. Kanubala — accepted AI-conference papers

2026

A Causal Framework to Measure and Mitigate Non-binary Treatment Discrimination

Ayan Majumdar, Deborah D. Kanubala, Kavya Gupta, Isabel Valera

AAAI 2026technical

Fairness studies of algorithmic decision-making systems often simplify complex decision processes, such as bail or lending decisions, into binary classification tasks (e.g., approve or not approve). However, these approaches overlook that such decisions are not inherently binary; they also involve n

Cited by 0 Source ↗PDF ↗Scholar ↗

2025

Accept or Deny? Evaluating LLM Fairness and Performance in Loan Approval across Table-to-Text Serialization Approaches

Israel Abebe Azime, Deborah D. Kanubala, Tejumade Afonja, Mario Fritz, Isabel Valera, Dietrich Klakow +1

EMNLP 2025

Large Language Models (LLMs) are increasingly employed in high-stakes decision-making tasks, such as loan approvals. While their applications expand across domains, LLMs struggle to process tabular data, ensuring fairness and delivering reliable predictions. In this work, we assess the performance a

Cited by 0 Source ↗PDF ↗Scholar ↗