2 citations · 6 across the 9 of their papers we have counts for
5 papers · 1 filter
Humble AI in the real-world: the case of algorithmic hiring
Rahul Nair, Inge Vejsbjerg, Elizabeth Daly +2
Humble AI (Knowles et al., 2023) argues for cautiousness in AI development and deployments through scepticism (accounting for limitations of statistical learning), curiosity (accou…
Paying Alignment Tax with Contrastive Learning
Buse Sibel Korkmaz, Rahul Nair, Elizabeth M. Daly +1
Current debiasing approaches often result a degradation in model capabilities such as factual accuracy and knowledge retention. Through systematic evaluation across multiple benchm…
Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic Hiring
Buse Sibel Korkmaz, Rahul Nair, Elizabeth M. Daly +3
Foundation models require fine-tuning to ensure their generative outputs align with intended results for specific tasks. Automating this fine-tuning process is challenging, as it t…
Black-box Uncertainty Quantification Method for LLM-as-a-Judge
Nico Wagner, Michael Desmond, Rahul Nair +6
LLM-as-a-Judge is a widely used method for evaluating the performance of Large Language Models (LLMs) across various tasks. We address the challenge of quantifying the uncertainty…
Explaining Knock-on Effects of Bias Mitigation
Svetoslav Nizhnichenkov, Rahul Nair, Elizabeth Daly +1
In machine learning systems, bias mitigation approaches aim to make outcomes fairer across privileged and unprivileged groups. Bias mitigation methods work in different ways and ha…