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Machine learning for modular multiplication
Motivated by cryptographic applications, we investigate two machine learning approaches to modular multiplication: namely circular regression and a sequence-to-sequence transformer model.
Prediction Sensitivity: Continual Audit of Counterfactual Fairness in Deployed Classifiers
As AI-based systems increasingly impact many areas of our lives, auditing these systems for fairness is an increasingly high-stakes problem.
Towards Auditability for Fairness in Deep Learning
Group fairness metrics can detect when a deep learning model behaves differently for advantaged and disadvantaged groups, but even models that score well on these metrics can make blatantly unfair predictions.
Towards a Measure of Individual Fairness for Deep Learning
Deep learning has produced big advances in artificial intelligence, but trained neural networks often reflect and amplify bias in their training data, and thus produce unfair predictions.
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