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Fairness

Papers

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Showing 1751–1800 of 5676 papers

TitleStatusHype
Scalable and Provably Fair Exposure Control for Large-Scale Recommender SystemsCode0
Demographic Bias of Expert-Level Vision-Language Foundation Models in Medical ImagingCode1
Towards Efficient Pareto-optimal Utility-Fairness between Groups in Repeated Rankings—0
LLM-Assisted Content Conditional Debiasing for Fair Text Embedding—0
Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness—0
Can One Embedding Fit All? A Multi-Interest Learning Paradigm Towards Improving User Interest Diversity Fairness—0
Fairness Rising from the Ranks: HITS and PageRank on Homophilic Networks—0
OpenHEXAI: An Open-Source Framework for Human-Centered Evaluation of Explainable Machine Learning—0
Fairness Risks for Group-conditionally Missing Demographics—0
Referee-Meta-Learning for Fast Adaptation of Locational Fairness—0
Bayesian Neural Networks with Domain Knowledge Priors—0
Evolutionary Reinforcement Learning: A Systematic Review and Future Directions—0
Stable matching as transport—0
Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluation—0
Toward Fairness via Maximum Mean Discrepancy Regularization on Logits Space—0
Fairness Without Harm: An Influence-Guided Active Sampling ApproachCode0
GRAPHGINI: Fostering Individual and Group Fairness in Graph Neural Networks—0
AI Fairness in Practice—0
FairProof : Confidential and Certifiable Fairness for Neural NetworksCode0
Leveraging Opposite Gender Interaction Ratio as a Path towards Fairness in Online Dating Recommendations Based on User Sexual Orientation—0
Causal Equal Protection as Algorithmic Fairness—0
Your Large Language Model is Secretly a Fairness Proponent and You Should Prompt it Like One—0
Endowing Pre-trained Graph Models with Provable FairnessCode0
Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness—0
ChatGPT Based Data Augmentation for Improved Parameter-Efficient Debiasing of LLMsCode0
Don't Go To Extremes: Revealing the Excessive Sensitivity and Calibration Limitations of LLMs in Implicit Hate Speech Detection—0
Fair Resource Allocation in Virtualized O-RAN Platforms—0
Fair Classification with Partial Feedback: An Exploration-Based Data Collection ApproachCode0
TuneTables: Context Optimization for Scalable Prior-Data Fitted NetworksCode1
The Male CEO and the Female Assistant: Evaluation and Mitigation of Gender Biases in Text-To-Image Generation of Dual Subjects—0
Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph ClusteringCode0
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning—0
FedD2S: Personalized Data-Free Federated Knowledge Distillation—0
FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation RetrievalCode0
On Explaining Unfairness: An Overview—0
InSaAF: Incorporating Safety through Accuracy and Fairness | Are LLMs ready for the Indian Legal Domain?—0
Privacy for Fairness: Information Obfuscation for Fair Representation Learning with Local Differential Privacy—0
Regulating Cryptocurrency and Decentralized Finance for an Inclusive Economy—0
"Model Cards for Model Reporting" in 2024: Reclassifying Category of Ethical Considerations in Terms of Trustworthiness and Risk Management—0
Prompt-Based Bias Calibration for Better Zero/Few-Shot Learning of Language Models—0
User Modeling and User Profiling: A Comprehensive Survey—0
DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service—0
Rate-Splitting Multiple Access for Quantized ISAC LEO Satellite Systems: A Max-Min Fair Energy-Efficient Beam Design—0
Stability and Multigroup Fairness in Ranking with Uncertain Predictions—0
Inference for an Algorithmic Fairness-Accuracy Frontier—0
Connecting Algorithmic Fairness to Quality Dimensions in Machine Learning in Official Statistics and Survey Production—0
MaxMin-RLHF: Alignment with Diverse Human Preferences—0
Is my Data in your AI Model? Membership Inference Test with Application to Face Images—0
Causal Learning for Trustworthy Recommender Systems: A Survey—0
Fairness Auditing with Multi-Agent CollaborationCode0
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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