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On The Impact of Machine Learning Randomness on Group Fairness
We investigate the impact on group fairness of different sources of randomness in training neural networks.
Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities
There is increasing attention being given to how to regulate AI systems.
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play
Standard empirical risk minimization (ERM) training can produce deep neural network (DNN) models that are accurate on average but under-perform in under-represented population subgroups, especially when there are imbalanced group distributions in the long-tailed training data.
Data Privacy and Trustworthy Machine Learning
The privacy risks of machine learning models is a major concern when training them on sensitive and personal data.
High Dimensional Model Explanations: an Axiomatic Approach
We propose a new axiomatization for a generalization of the Banzhaf index; our method can also be thought of as an approximation of a black-box model by a higher-order polynomial.
On the Privacy Risks of Model Explanations
We analyze connections between model explanations and the leakage of sensitive information about the model's training set.
Axiomatic Characterization of Data-Driven Influence Measures for Classification
We study the following problem: given a labeled dataset and a specific datapoint x, how did the i-th feature influence the classification for x?
