Inherent Trade-Offs in the Fair Determination of Risk Scores

Jon Kleinberg, Sendhil Mullainathan, Manish Raghavan

DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)

Artificial IntelligenceEngineeringRisk MetricComputational ComplexityData Science +21

Mitigating bias in algorithmic hiring

Manish Raghavan, Solon Barocas, Jon Kleinberg, +1

EngineeringHuman Resource ManagementTechnology LawBusiness AnalyticsAlgorithmic Hiring +17

Inherent Trade-Offs in the Fair Determination of Risk Scores

Jon Kleinberg, Sendhil Mullainathan, Manish Raghavan

arXiv (Cornell University)

Artificial IntelligenceEngineeringRisk MetricComputational ComplexityData Science +21

On Fairness and Calibration

Geoff Pleiss, Manish Raghavan, Felix Wu, +2

arXiv (Cornell University)

EngineeringMachine LearningSingle Error ConstraintData ScienceClass Imbalance +15

The hidden assumptions behind counterfactual explanations and principal reasons

Solon Barocas, Andrew D. Selbst, Manish Raghavan

Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices

Manish Raghavan, Solon Barocas, Jon Kleinberg, +1

arXiv (Cornell University)

EngineeringHuman Resource ManagementTechnology LawBusiness AnalyticsAlgorithmic Hiring +17

Algorithmic monoculture and social welfare

Jon Kleinberg, Manish Raghavan

Proceedings of the National Academy of Sciences

Model Multiplicity: Opportunities, Concerns, and Solutions

Emily Black, Manish Raghavan, Solon Barocas

2022 ACM Conference on Fairness, Accountability, and Transparency

EngineeringBiasManagementEconomic AnalysisFairness (Computer Systems) +16

Human bias in algorithm design

Carey K. Morewedge, Sendhil Mullainathan, Haaya Naushan, +4

Nature Human Behaviour

Artificial IntelligenceCognitive ScienceAlgorithmic BiasBiasDesign +6

Deduplicating a places database

Nilesh Dalvi, Marian Olteanu, Manish Raghavan, +1

EngineeringGeographic Information RetrievalCentral ParkLocalizationLocation-based Service +18