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Probably Approximately Metric-Fair Learning · Study

WorkProbably Approximately Metric-Fair Learning

Gal Yona2018

Work2018

Probably Approximately Metric-Fair Learning

Gal Yona · Guy N. Rothblum

21 citations·0/0 files stored·International Conference on Machine Learning

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Ethics and Social Impacts of AI Privacy-Preserving Technologies in Data Machine Learning and Algorithms

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2021A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle 2020Algorithmic Fairness: Choices, Assumptions, and Definitions 2020A snapshot of the frontiers of fairness in machine learning 2020Review of Mathematical frameworks for Fairness in Machine Learning 2021SoK: Machine Learning Governance 2019Learning from Outcomes: Evidence-Based Rankings 2020Moment Multicalibration for Uncertainty Estimation 2020Metric-Free Individual Fairness in Online Learning

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Year2018

Typeother

Published2018-07-03

VenueInternational Conference on Machine Learning

Languageen

Citations21

Files0 public / 0

ShelfEthics and Social Impacts of AI

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openalex_workW2963475122

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