Monotone Individual Fairness: Online Learning with Auditors' Feedback
Concepts de base
Individual fairness auditing schemes aim to balance predictive accuracy and fairness violations, enhancing online learning algorithms.
Résumé
The content delves into the concept of individual fairness in online learning, focusing on auditing schemes for detecting violations. It introduces a novel framework for auditing unfairness, presents oracle-efficient algorithms, and discusses the complexity of auditing. The study aims to achieve simultaneous no-regret guarantees for accuracy and fairness in online learning settings.
Introduction:
Increasing use of algorithms in critical decision-making domains.
Formalizing notions of fairness and proposing accurate algorithms.
Individual Fairness and Monotone Auditing Schemes:
Defining fairness violations and auditors' roles.
Introducing monotone aggregation functions for auditing schemes.
Online Learning with Individual Fairness:
Defining misclassification and unfairness losses.
Formulating learning objectives for simultaneous no-regret guarantees.
Partial Information:
Policies refraining from violations slightly below sensitivity level α.
Conclusion and Future Directions:
Challenges in achieving simultaneous accuracy-fairness objectives.
Monotone Individual Fairness
Stats
Using our generalized framework, we present an oracle-efficient algorithm achieving an upper bound of O(√T) for regret...
Our construction will only require making ˜O(α−2) calls to an optimization oracle on every round...
How might advancements in machine learning impact the evolution of individual fairness principles
機械学習技術の進歩は個別公平原則の進化に大きな影響を与え得ます。例えば、「同じような人物は同じよう」原則を強化し適用範囲拡大させる新たな手法やアルゴリズムが開発されています。また、AIエシックスおよびフェアネス向上プログラム等多岐済み分野でも活用され始めています。
この先ではAI技術そのも変革力強化しなり「Fairness by Design」コンセプト普及広まり予想されます。
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Table des matières
Monotone Individual Fairness: Online Learning with Auditors' Feedback
Monotone Individual Fairness
How can the concept of individual fairness be practically implemented beyond theoretical frameworks
What potential biases or limitations could arise from relying on auditors' feedback for detecting fairness violations
How might advancements in machine learning impact the evolution of individual fairness principles