The content delves into the intricate balance between predictive accuracy and individual fairness in online learning. It introduces a novel auditing scheme that aggregates feedback from multiple auditors to ensure fair treatment of similar individuals. The algorithms presented offer significant improvements in computational efficiency and address the challenges posed by real-world data generation assumptions.
The work emphasizes the importance of considering individual fairness from the perspective of the learner, highlighting the need to treat similar individuals similarly. By extending previous frameworks and introducing monotone aggregation functions, the author provides insights into achieving simultaneous no-regret guarantees while minimizing fairness violations. The study also discusses practical applications such as online classification settings with label feedback constraints.
Overall, this content provides a comprehensive analysis of monotone individual fairness in online learning, offering new perspectives on algorithmic fairness and decision-making processes.
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by Yahav Bechav... at arxiv.org 03-12-2024
https://arxiv.org/pdf/2403.06812.pdfDeeper Inquiries