Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness

Michael Kearns, Seth Neel, Aaron Roth, +1

arXiv (Cornell University)

EngineeringMachine LearningDiscriminationLawData Science +15

A Convex Framework for Fair Regression

Richard A. Berk, Hoda Heidari, Shahin Jabbari, +5

arXiv (Cornell University)

EngineeringMachine LearningFair RegressionFairness RegularizersData Science +14

Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness

Michael Kearns, Seth Neel, Aaron Roth, +1

International Conference on Machine Learning

Best Fair ClassifierMachine LearningEngineeringDiscriminationLaw +16

Fair Algorithms for Learning in Allocation Problems

Hadi Elzayn, Shahin Jabbari, Christopher Jung, +4

EngineeringGame TheoryDiscriminationCentralized AgentLaw +16

Descent-to-Delete: Gradient-Based Methods for Machine Unlearning

Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi

arXiv (Cornell University)

Convex ModelsEngineeringMachine LearningData ScienceData Mining +11

Rawlsian Fairness for Machine Learning.

Matthew Joseph, Michael Kearns, Jamie Morgenstern, +2

arXiv (Cornell University)

Artificial IntelligenceEngineeringMachine LearningTechnical DefinitionGame Theory +18

Adaptive Machine Unlearning

Varun Gupta, Christopher Jung, Seth Neel, +3

arXiv (Cornell University)

Artificial IntelligencePrivacy ProtectionEngineeringMachine LearningAdaptive Computing +17

Eliciting and Enforcing Subjective Individual Fairness.

Christopher Jung, Michael Kearns, Seth Neel, +3

arXiv (Cornell University)

Mechanism DesignIndividual FairnessEngineeringSubjective FairnessAlgorithmic Bias +11

Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM

Katrina Ligett, Seth Neel, Aaron Roth, +2

arXiv (Cornell University)

Meritocratic Fairness for Infinite and Contextual Bandits

Matthew Joseph, Michael Kearns, Jamie Morgenstern, +2

Meritocratic FairnessContextual BanditOnline Linear SettingOnline AlgorithmGame Theory +11