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Found 4 papers, 0 papers with code
Comparing interpretability and explainability for feature selection
In this paper, we investigate the performance of variable importance as a feature selection method across various black-box and interpretable machine learning methods.
Detecting Racial Bias in Jury Selection
To support the 2019 U. S. Supreme Court case "Flowers v. Mississippi", APM Reports collated historical court records to assess whether the State exhibited a racial bias in striking potential jurors.
Interpretable Predictive Maintenance for Hard Drives
Existing machine learning approaches for data-driven predictive maintenance are usually black boxes that claim high predictive power yet cannot be understood by humans.
Optimal Policy Trees
We propose an approach for learning optimal tree-based prescription policies directly from data, combining methods for counterfactual estimation from the causal inference literature with recent advances in training globally-optimal decision trees.
