Case of Predictive Parity
Post-processing is a bias mitigation technique proposed by the algorithmic fairness community to ensure the fairness of decision making systems that rely on machine learning (ML).
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Sep 22, 2024 · In this article, I will discuss our recent work on designing post-processing algorithms for fair classification, which can be applied to a wide range of ...
To this end, this paper provides a complete characterization of the inherent tradeoff of demographic parity on classification problems, under the most general ...
May 7, 2024 · This paper describes a differentially private post-processing algorithm for learning fair regressors satisfying statistical parity, addressing ...
Nov 26, 2023 · Abstract: To mitigate the bias exhibited by machine learning models, fairness criteria can be integrated into the training process to ensure ...
Missing: Case Predictive Parity.
Current legislation ensures fairness by barring algorithm designers from using demographic information in their decision-making. As a result, the algorithms ...
Mar 4, 2024 · Our review identified n=9, 2, and 2 studies that employed pre-processing, in-processing, and post-processing techniques for ML bias mitigation.
This issue has become a major obstacle to the deployment of machine learning systems in high‐stakes domains, for example, criminal judgment, medical testing, ...
A parity measure is a simple observational criterion that requires on the evaluation metrics to be independent of the salient group A.
Aug 1, 2024 · Generally, techniques to promote fairness can be implemented in the following stages of the AI process: preprocessing, processing (also ...