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Found 9 papers, 2 papers with code
Semiparametric Efficient Inference in Adaptive Experiments
This central limit theorem enables efficient inference at fixed sample sizes.
FairWASP: Fast and Optimal Fair Wasserstein Pre-processing
FairWASP can therefore be used to construct datasets which can be fed into any classification method, not just methods which accept sample weights.
Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access
Recent work has sought to train fair models without sensitive attributes on training data.
Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale
We further show that MinDiff optimization is very sensitive to choice of batch size in the under-parameterized regime.
Flexible Group Fairness Metrics for Survival Analysis
In this paper we explore how to utilise existing survival metrics to measure bias with group fairness metrics.
Fair When Trained, Unfair When Deployed: Observable Fairness Measures are Unstable in Performative Prediction Settings
These measures are sensitive to distribution shift: a predictor which is trained to satisfy one of these fairness definitions may become unfair if the distribution changes.
FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomes
Our framework can simultaneously target multiple observable or counterfactual fairness criteria, and it enables users to combine a large number of previously trained and newly trained predictors.
Counterfactual Risk Assessments, Evaluation, and Fairness
These tools thus reflect risk under the historical policy, rather than under the different decision options that the tool is intended to inform.
Filtering Tweets for Social Unrest
Since the events of the Arab Spring, there has been increased interest in using social media to anticipate social unrest.
