Fairness

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 …

Presented Master thesis work as part of my defense.

Project that was conducted as part of my Master’s thesis to explore the application of generative models to compute counterfactuals for fairness.

This project explores the case for potential bias in generative models such as variational autoencoders. The project also briefly looks at ways to mitigate such bias.

Mini-project that looks at potential bias in pre-trained word embeddings and methods on how to remove such bias.

Poster presentation on the work related to counterfactuals for fairness.