Active Fairness in Algorithmic Decision Making
Name
3306618.3314277.pdf
Description
Published version
Size
1.61 MB
Format
Adobe PDF
Checksum (MD5)
32c96bcbfbb3539a07d66d1c32db2289
Author(s)
Noriega-Campero, Alejandro
Bakker, Michiel A
Garcia-Bulle, Bernardo
Pentland, Alex 'Sandy'
Date Issued
2019
Journal
AIES 2019 - Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society
Publisher
Association for Computing Machinery (ACM)
Citation
Noriega-Campero, Alejandro, Bakker, Michiel A, Garcia-Bulle, Bernardo and Pentland, Alex 'Sandy'. 2019. "Active Fairness in Algorithmic Decision Making." AIES 2019 - Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society.
Version
Final published version
Abstract
© 2019 Copyright held by the owner/author(s). Society increasingly relies on machine learning models for automated decision making. Yet, efficiency gains from automation have come paired with concern for algorithmic discrimination that can systematize inequality. Recent work has proposed optimal post-processing methods that randomize classification decisions for a fraction of individuals, in order to achieve fairness measures related to parity in errors and calibration. These methods, however, have raised concern due to the information inefficiency, intra-group unfairness, and Pareto sub-optimality they entail. The present work proposes an alternative active framework for fair classification, where, in deployment, a decision-maker adaptively acquires information according to the needs of different groups or individuals, towards balancing disparities in classification performance. We propose two such methods, where information collection is adapted to group- and individual-level needs respectively. We show on real-world datasets that these can achieve: 1) calibration and single error parity (e.g., equal opportunity); and 2) parity in both false positive and false negative rates (i.e., equal odds). Moreover, we show that by leveraging their additional degree of freedom, active approaches can substantially outperform randomization-based classifiers previously considered optimal, while avoiding limitations such as intra-group unfairness.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1145/3306618.3314277
