SOTAVerified

Fairness

Papers

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Showing 3726–3750 of 5676 papers

TitleStatusHype
Bayes-Optimal Classifiers under Group FairnessCode0
The four-fifths rule is not disparate impact: a woeful tale of epistemic trespassing in algorithmic fairness—0
Stochastic Perturbations of Tabular Features for Non-Deterministic Inference with AutomungeCode0
Human-Algorithm Collaboration: Achieving Complementarity and Avoiding UnfairnessCode0
Are There Exceptions to Goodhart's Law? On the Moral Justification of Fairness-Aware Machine Learning—0
On Guiding Visual Attention with Language Specification—0
Fairness constraint in Structural Econometrics and Application to fair estimation using Instrumental Variables—0
On Learning and Enforcing Latent Assessment Models using Binary Feedback from Human Auditors Regarding Black-Box Classifiers—0
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision—0
Modelling multi-cell edge video analytics—0
Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey—0
Bias and unfairness in machine learning models: a systematic literature review—0
Trustworthy Anomaly Detection: A Survey—0
Fairness Indicators for Systematic Assessments of Visual Feature Extractors—0
Quantifying Memorization Across Neural Language ModelsCode2
Choosing an algorithmic fairness metric for an online marketplace: Detecting and quantifying algorithmic bias on LinkedIn—0
Fairness Amidst Non-IID Graph Data: A Literature Review—0
Fairness-aware Configuration of Machine Learning LibrariesCode0
Probabilistic fair behaviors spark its boost in the Ultimatum Game: the strength of good Samaritans—0
Fair When Trained, Unfair When Deployed: Observable Fairness Measures are Unstable in Performative Prediction Settings—0
Proceedings of the Robust Artificial Intelligence System Assurance (RAISA) Workshop 2022—0
Designing Closed Human-in-the-loop Deferral PipelinesCode0
Prediction Sensitivity: Continual Audit of Counterfactual Fairness in Deployed Classifiers—0
An Exploration of Multicalibration Uniform Convergence Bounds—0
Obtaining Dyadic Fairness by Optimal TransportCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)99.86—Unverified
21D-CSNNPredictive Equality (age)97.8—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)96.87—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)98.97—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)98.45—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)98.68—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)99.31—Unverified
#ModelMetricClaimedVerifiedStatus
1Neighbour LearningDegree of Bias (DoB)0.49—Unverified
#ModelMetricClaimedVerifiedStatus
1Neighbour LearningDegree of Bias (DoB)6.26—Unverified
#ModelMetricClaimedVerifiedStatus
1Neighbour LearningDegree of Bias (DoB)1.96—Unverified