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Found 5 papers, 3 papers with code
Estimating and Implementing Conventional Fairness Metrics With Probabilistic Protected Features
Unlike similar existing approaches, our methods take advantage of contextual information -- specifically, the relationships between a model's predictions and the probabilistic prediction of protected attributes, given the true protected attribute, and vice versa -- to provide tighter bounds on the true disparity.
ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription
ODTLearn is an open-source Python package that provides methods for learning optimal decision trees for high-stakes predictive and prescriptive tasks based on the mixed-integer optimization (MIO) framework proposed in Aghaei et al. (2019) and several of its extensions.
Fairness in Contextual Resource Allocation Systems: Metrics and Incompatibility Results
We study critical systems that allocate scarce resources to satisfy basic needs, such as homeless services that provide housing.
Learning Optimal Fair Classification Trees: Trade-offs Between Interpretability, Fairness, and Accuracy
The increasing use of machine learning in high-stakes domains -- where people's livelihoods are impacted -- creates an urgent need for interpretable, fair, and highly accurate algorithms.
Learning Optimal Prescriptive Trees from Observational Data
We consider the problem of learning an optimal prescriptive tree (i. e., an interpretable treatment assignment policy in the form of a binary tree) of moderate depth, from observational data.
