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Found 8 papers, 2 papers with code
Feature-Wise Bias Amplification
This overestimation gives rise to feature-wise bias amplification -- a previously unreported form of bias that can be traced back to the features of a trained model.
Supervising Feature Influence
Standard methods for training classifiers that minimize empirical risk do not constrain the behavior of the classifier on such datapoints.
Influence-Directed Explanations for Deep Convolutional Networks
We study the problem of explaining a rich class of behavioral properties of deep neural networks.
Latent Factor Interpretations for Collaborative Filtering
The interpretation of latent factors can then replace the uninterpreted latent factors, resulting in a new model that expresses predictions in terms of interpretable features.
Case Study: Explaining Diabetic Retinopathy Detection Deep CNNs via Integrated Gradients
In this report, we applied integrated gradients to explaining a neural network for diabetic retinopathy detection.
Proxy Non-Discrimination in Data-Driven Systems
Machine learnt systems inherit biases against protected classes, historically disparaged groups, from training data.
Use Privacy in Data-Driven Systems: Theory and Experiments with Machine Learnt Programs
For a specific instantiation of this definition, we present a program analysis technique that detects instances of proxy use in a model, and provides a witness that identifies which parts of the corresponding program exhibit the behavior.
Debugging Machine Learning Tasks
Unlike traditional programs (such as operating systems or word processors) which have large amounts of code, machine learning tasks use programs with relatively small amounts of code (written in machine learning libraries), but voluminous amounts of data.
