Can we trust fairness?

Although neural networks achieve incredible performance in various domains and are actively employed in high societal impact areas, their unfairness still remains a problem. Most existing computational techniques for limiting unfairness are constraint-based or regularization-based. However, often these methods work in unexpected ways and achieve fairness by introducing more complex biases into decision-making algorithm. The goal of this project is to investigate unintended consequences of fairness constraints applied to convolutional neural networks and facial recognition systems in particular.

Valeria Cherepanova
Valeria Cherepanova
PhD Student in Applied Math

My research focuses on adversarial machine learning and fairness in deep learning