Fairness

The growing complexity of content moderation policies presents a critical challenge for their consistent operationalization. While …

Algorithmic recourse aims to provide actionable recommendations that enable individuals to change unfavorable model outcomes, and prior …

Large-scale web-scraped text corpora used to train general-purpose AI models often contain harmful demographic-targeted social biases, …

Fairness studies of algorithmic decision-making systems often simplify complex decision processes, such as bail or loan approvals, into …

We conduct a comprehensive benchmark study evaluating LLMs for demographic-targeted social bias detection in raw text data, revealing that while certain configurations show promise for scale, significant performance gaps persist across complex social categories.

Virtual invited presentation of our project on the usefulness of unlabeled data when modeling fair decision-making policies.

Virtual presentation of our project on the usefulness of unlabeled data when modeling fair decision-making policies.

Decision making algorithms, in practice, are often trained on data that exhibits a variety of biases. Decision-makers often aim to take …

We explore the helpfulness of unlabeled data for fair, optimal and stable decision-making in societal settings.

Notions of causal fairness for algorithmic decision making systems crucially rely on estimating whether an individual (or a group of …