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This paper develops methods to post-process a prediction function to achieve two causal fairness criteria: "equal counterfactual opportunity'' and " ...
We consider a causal model of the ML decisions, define fairness through counterfactual decisions within the model, and then form algorithmic decisions that ...
In this paper, we propose two algorithms that adjust fitted ML predictors to produce decisions that are fair. Our methods provide post-hoc adjustments to the ...
Two algorithms are proposed that adjust ML predictors to produce decisions that are fair, and the trade-o between accuracy and fairness, is evaluated on ...
May 26, 2019 · In this paper, we propose two algorithms that adjust fitted ML predictors to make them fair. We focus on two legal notions of fairness: (a) ...
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Oct 9, 2024 · Learn how to use the counterfactual fairness metric to evaluate ML model predictions for fairness, and its benefits and drawbacks.
Missing: Adjusting | Show results with:Adjusting
Oct 9, 2024 · Learn how to use the equality of opportunity metric to evaluate ML model predictions for fairness, and its benefits and drawbacks.
Missing: Adjusting | Show results with:Adjusting
Oct 11, 2024 · Research interest in exploring, measuring, and ensuring AI fairness has grown rapidly in recent years. To name a few, Fair Machine Learning ( ...
Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, ...
Two algorithms are proposed that adjust fitted ML predictors to make them fair on two legal notions of fairness: providing equal opportunity (EO) to ...