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LiFT: A Scalable Framework for Measuring Fairness in ML Applications.

, and . CIKM, page 2773-2780. ACM, (2020)

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Models and algorithms for data privacy.. Stanford University, USA, (2006)Bringing Salary Transparency to the World: Computing Robust Compensation Insights via LinkedIn Salary., , , and . CIKM, page 447-455. ACM, (2017)Our Data, Ourselves: Privacy Via Distributed Noise Generation., , , , and . EUROCRYPT, volume 4004 of Lecture Notes in Computer Science, page 486-503. Springer, (2006)Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models., , , , , , and . KDD, page 3671-3681. ACM, (2022)Multiaccurate Proxies for Downstream Fairness., , , , , and . FAccT, page 1207-1239. ACM, (2022)Designing Closed-Loop Models for Task Allocation., , , and . HHAI, volume 368 of Frontiers in Artificial Intelligence and Applications, page 17-32. IOS Press, (2023)Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned., , , , and . KDD, page 3205-3206. ACM, (2019)Tutorials at The Web Conference 2023., , , , , , , , , and 86 other author(s). WWW (Companion Volume), page 648-658. ACM, (2023)PriPeARL: A Framework for Privacy-Preserving Analytics and Reporting at LinkedIn., and . CIKM, page 2183-2191. ACM, (2018)Quality of textbooks: an empirical study., , , , and . ACM DEV, page 16:1. ACM, (2012)