SOTAVerified

Fairness

Papers

Showing 15761600 of 5676 papers

TitleStatusHype
Exploring Neural Joint Activity in Spiking Neural Networks for Fraud DetectionCode0
CILIATE: Towards Fairer Class-based Incremental Learning by Dataset and Training RefinementCode0
Equal Confusion Fairness: Measuring Group-Based Disparities in Automated Decision SystemsCode0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated LearningCode0
Towards Energy-Aware Federated Learning on Battery-Powered ClientsCode0
Optimal Transport on Categorical Data for Counterfactuals using Compositional Data and Dirichlet TransportCode0
A General Approach for Computing a Consensus in Group Decision Making That Integrates Multiple Ethical PrinciplesCode0
EqGNN: Equalized Node Opportunity in GraphsCode0
Achieving Group Fairness through Independence in Predictive Process MonitoringCode0
Equal Experience in Recommender SystemsCode0
Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node InjectionsCode0
Edge-Colored Clustering in Hypergraphs: Beyond Minimizing Unsatisfied EdgesCode0
Pairwise Fairness for Ordinal RegressionCode0
Pantypes: Diverse Representatives for Self-Explainable ModelsCode0
Are You Getting What You Pay For? Auditing Model Substitution in LLM APIsCode0
Enumerating Fair Packages for Group RecommendationsCode0
Equal Improvability: A New Fairness Notion Considering the Long-term ImpactCode0
Enrolment-based personalisation for improving individual-level fairness in speech emotion recognitionCode0
ChatGPT Based Data Augmentation for Improved Parameter-Efficient Debiasing of LLMsCode0
Ensuring Fairness Beyond the Training DataCode0
A Causal Framework to Measure and Mitigate Non-binary Treatment DiscriminationCode0
Chasing Fairness Under Distribution Shift: A Model Weight Perturbation ApproachCode0
Enhancing the Accuracy and Fairness of Human Decision MakingCode0
Entity-Switched Datasets: An Approach to Auditing the In-Domain Robustness of Named Entity Recognition ModelsCode0
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Benchmark Results

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