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Found 5 papers, 1 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.
Entropy Regularization for Population Estimation
Entropy regularization is known to improve exploration in sequential decision-making problems.
Forecasting Algorithms for Causal Inference with Panel Data
Conducting causal inference with panel data is a core challenge in social science research.
Algorithmic Fairness and Vertical Equity: Income Fairness with IRS Tax Audit Models
First, we show how the use of more flexible machine learning (classification) methods -- as opposed to simpler models -- shifts audit burdens from high to middle-income taxpayers.
Integrating Reward Maximization and Population Estimation: Sequential Decision-Making for Internal Revenue Service Audit Selection
This approach has the potential to improve audit efficacy, while maintaining policy-relevant estimates of the tax gap.
