Irtiza Chowdhury — accepted AI-conference papers

2025

Fairness Beyond Performance: Revealing Reliability Disparities Across Groups in Legal NLP

ACL 2025long

Fairness in NLP must extend beyond performance parity to encompass equitable reliability across groups. This study exposes a criticalblind spot: models often make less reliable or overconfident predictions for marginalized groups, even when overall performance appearsfair. Using the FairLex benchmar…

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2024

The Craft of Selective Prediction: Towards Reliable Case Outcome Classification - An Empirical Study on European Court of Human Rights Cases

Santosh T.y.s.s, Irtiza Chowdhury, Shanshan Xu, Matthias Grabmair

EMNLP 2024finding

In high-stakes decision-making tasks within legal NLP, such as Case Outcome Classification (COC), quantifying a model’s predictive confidence is crucial. Confidence estimation enables humans to make more informed decisions, particularly when the model’s certainty is low, or where the consequences of…

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