FairBED quantifies dataset fairness as uninformative about sensitive attributes and uses fairness-aware BED to gather data yielding better fairness-accuracy trade-offs than random or standard BED acquisition.
Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian
6 Pith papers cite this work. Polarity classification is still indexing.
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SCC-VFL reduces individual decision flip rates by up to 98% in vertical federated learning while preserving accuracy through differentially private feature role discovery and selective counterfactual consistency enforcement.
Formalizing personalization as individual actionability in causal recourse shows hard constraints degrade validity and plausibility while revealing socio-demographic disparities in costs.
The paper introduces a sequential generalized likelihood-ratio test framework for auditing Statistical Parity and Equal Opportunity fairness metrics under limited model query access.
Synthetic data generation exhibits disparate impact from group-specific approximation, sampling, and estimation errors; group-wise models improve both utility and parity on graphical model methods.
Proposes a formal DP-compatible framework with three unfairness measures (mutual information with TV proxy, MaxSAT-based repair, top-k tuple contribution) that satisfy positivity, monotonicity, and computability.
citing papers explorer
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FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data
FairBED quantifies dataset fairness as uninformative about sensitive attributes and uses fairness-aware BED to gather data yielding better fairness-accuracy trade-offs than random or standard BED acquisition.
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Toward Individual Fairness Without Centralized Data: Selective Counterfactual Consistency for Vertical Federated Learning
SCC-VFL reduces individual decision flip rates by up to 98% in vertical federated learning while preserving accuracy through differentially private feature role discovery and selective counterfactual consistency enforcement.
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From Universal to Individualized Actionability: Revisiting Personalization in Algorithmic Recourse
Formalizing personalization as individual actionability in causal recourse shows hard constraints degrade validity and plausibility while revealing socio-demographic disparities in costs.
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Sequential Fairness Auditing with Limited Output Access
The paper introduces a sequential generalized likelihood-ratio test framework for auditing Statistical Parity and Equal Opportunity fairness metrics under limited model query access.
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Disparate Impact in Synthetic Data Generation
Synthetic data generation exhibits disparate impact from group-specific approximation, sampling, and estimation errors; group-wise models improve both utility and parity on graphical model methods.
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Measuring Database Unfairness via Dependency Quantification Under Differential Privacy
Proposes a formal DP-compatible framework with three unfairness measures (mutual information with TV proxy, MaxSAT-based repair, top-k tuple contribution) that satisfy positivity, monotonicity, and computability.