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Fairness

Papers

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Showing 3901–3950 of 5676 papers

TitleStatusHype
Visualizing chest X-ray dataset biases using GANs—0
Visual Representation Learning over Latent Domains—0
Voluntary Information Disclosure in Centralized Matching: Efficiency Gains and Strategic Properties—0
VTruST: Controllable value function based subset selection for Data-Centric Trustworthy AI—0
WAFFLe: Weight Anonymized Factorization for Federated Learning—0
Walk a Mile in Their Shoes: a New Fairness Criterion for Machine Learning—0
Wasserstein-based fairness interpretability framework for machine learning models—0
Wasserstein Robust Classification with Fairness Constraints—0
WassFFed: Wasserstein Fair Federated Learning—0
Watching the Watchers: A Comparative Fairness Audit of Cloud-based Content Moderation Services—0
WCLD: Curated Large Dataset of Criminal Cases from Wisconsin Circuit Courts—0
Weighted Notions of Fairness with Binary Supermodular Chores—0
Weighted Sampled Split Learning (WSSL): Balancing Privacy, Robustness, and Fairness in Distributed Learning Environments—0
Welfare and Distributional Impacts of Fair Classification—0
Welfare and Fairness Dynamics in Federated Learning: A Client Selection Perspective—0
Welfare Measure for Resource Allocation with Algorithmic Implementation: Beyond Average and Max-Min—0
What About Applied Fairness?—0
What are you optimizing for? Aligning Recommender Systems with Human Values—0
What do End-to-End Speech Models Learn about Speaker, Language and Channel Information? A Layer-wise and Neuron-level Analysis—0
What do people expect from Artificial Intelligence? Public opinion on alignment in AI moderation from Germany and the United States—0
What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective—0
What is Fair? Exploring Pareto-Efficiency for Fairness Constrained Classifiers—0
What Is Fairness? On the Role of Protected Attributes and Fictitious Worlds—0
What is Left After Distillation? How Knowledge Transfer Impacts Fairness and Bias—0
What Is the Point of Equality in Machine Learning Fairness? Beyond Equality of Opportunity—0
What is the Point of Fairness? Disability, AI and The Complexity of Justice—0
What Role Do Intelligent Reflecting Surfaces Play in Multi-Antenna Non-Orthogonal Multiple Access?—0
What Should We Optimize in Participatory Budgeting? An Experimental Study—0
What's in a Query: Polarity-Aware Distribution-Based Fair Ranking—0
What's Sex Got To Do With Fair Machine Learning?—0
What's the Problem, Linda? The Conjunction Fallacy as a Fairness Problem—0
"What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairness—0
What will it take to generate fairness-preserving explanations?—0
When Can Memorization Improve Fairness?—0
Whence Is A Model Fair? Fixing Fairness Bugs via Propensity Score Matching—0
When Collaborative Filtering is not Collaborative: Unfairness of PCA for Recommendations—0
When Counterfactual Reasoning Fails: Chaos and Real-World Complexity—0
When does a predictor know its own loss?—0
Does Data Augmentation Improve Generalization in NLP?—0
When Fairness Isn't Statistical: The Limits of Machine Learning in Evaluating Legal Reasoning—0
When the Umpire is also a Player: Bias in Private Label Product Recommendations on E-commerce Marketplaces—0
When Transliteration Met Crowdsourcing : An Empirical Study of Transliteration via Crowdsourcing using Efficient, Non-redundant and Fair Quality Control—0
When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness—0
Where Are We? Using Scopus to Map the Literature at the Intersection Between Artificial Intelligence and Research on Crime—0
Where Is the Normative Proof? Assumptions and Contradictions in ML Fairness Research—0
Where to locate COVID-19 mass vaccination facilities?—0
Which Demographic Features Are Relevant for Individual Fairness Evaluation of U.S. Recidivism Risk Assessment Tools?—0
Whistleblower protection in the digital age -- why 'anonymous' is not enough. From technology to a wider view of governance—0
Whither Bias Goes, I Will Go: An Integrative, Systematic Review of Algorithmic Bias Mitigation—0
Whither Fair Clustering?—0
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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