← Application Areas
Machine Learning › Application Areas ›

Fairness

3337 directly classified papers

Papers per year

Papers

Evaluating Large Language Models for Health-related Queries with Presuppositions ACL 2024
Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models ACL 2024
Debiasing Large Language Models with Structured Knowledge ACL 2024
Don’t Go To Extremes: Revealing the Excessive Sensitivity and Calibration Limitations of LLMs in Implicit Hate Speech Detection ACL 2024
Leveraging Diffusion Perturbations for Measuring Fairness in Computer Vision AAAI 2024
Measuring Political Bias in Large Language Models: What Is Said and How It Is Said ACL 2024
Fairness without Demographics through Shared Latent Space-Based Debiasing AAAI 2024
Investigating Data Contamination in Modern Benchmarks for Large Language Models NAACL 2024
Fairness under Covariate Shift: Improving Fairness-Accuracy Tradeoff with Few Unlabeled Test Samples AAAI 2024
A Sequentially Fair Mechanism for Multiple Sensitive Attributes AAAI 2024
Bias in Motion: Theoretical Insights into the Dynamics of Bias in SGD Training NIPS 2024
Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications NAACL 2024
Towards Optimal Subsidy Bounds for Envy-Freeable Allocations AAAI 2024
Proportional Representation in Metric Spaces and Low-Distortion Committee Selection AAAI 2024
REGLO: Provable Neural Network Repair for Global Robustness Properties AAAI 2024
Implications of Distance over Redistricting Maps: Central and Outlier Maps AAAI 2024
Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces AAAI 2024
Mitigating Label Bias in Machine Learning: Fairness through Confident Learning AAAI 2024
FedGCR: Achieving Performance and Fairness for Federated Learning with Distinct Client Types via Group Customization and Reweighting AAAI 2024
FairTrade: Achieving Pareto-Optimal Trade-Offs between Balanced Accuracy and Fairness in Federated Learning AAAI 2024
No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation AAAI 2024
Intelligent Calibration for Bias Reduction in Sentiment Corpora Annotation Process AAAI 2024
Proportional Fairness in Non-Centroid Clustering NIPS 2024
Eliciting Honest Information from Authors Using Sequential Review AAAI 2024
Greedy-Based Online Fair Allocation with Adversarial Input: Enabling Best-of-Many-Worlds Guarantees AAAI 2024