
Distributional properties of training data, such as the diversity of the population represented, are crucial for ensuring fairness and accountability in machine learning models. This paper introduces the novel notion of ML property attestation, which allows a model trainer to demonstrate relevant properties of a model to a verifier while preserving the confidentiality of sensitive training data.


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attesting-distributional-properties-of-training-data-for-machine-learning-to-ensure-fairness-and-accountability


Attesting Distributional Properties of Training Data for Machine Learning to Ensure Fairness and Accountability
