Priority-based Post-Processing Bias Mitigation for Individual and Group Fairness

Previous post-processing bias mitigation algorithms on both group and individual fairness don't work on regression models and datasets with multi-class numerical labels. We propose a priority-based post-processing bias mitigation on both group and individual fairness with the notion that simila...

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Bibliographic Details
Published inarXiv.org
Main Author Lohia, Pranay
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 31.01.2021
Subjects
Algorithms
Bias
Crime
Datasets
Economic factors
Post-production processing
Regression models
Segmentation
Smart grid
Social factors
Tariffs
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Summary:Previous post-processing bias mitigation algorithms on both group and individual fairness don't work on regression models and datasets with multi-class numerical labels. We propose a priority-based post-processing bias mitigation on both group and individual fairness with the notion that similar individuals should get similar outcomes irrespective of socio-economic factors and more the unfairness, more the injustice. We establish this proposition by a case study on tariff allotment in a smart grid. Our novel framework establishes it by using a user segmentation algorithm to capture the consumption strategy better. This process ensures priority-based fair pricing for group and individual facing the maximum injustice. It upholds the notion of fair tariff allotment to the entire population taken into consideration without modifying the in-built process for tariff calculation. We also validate our method and show superior performance to previous work on a real-world dataset in criminal sentencing.
ISSN:2331-8422