Fairness

Bias Neutralization in Non-Parallel Texts: A Cyclic Approach with Auxiliary Guidance EMNLP 2023 Bias On Demand: Investigating Bias with a Synthetic Data Generator IJCAI 2023 Bias Propagation in Federated Learning ICLR 2023 BiasX: “Thinking Slow” in Toxic Content Moderation with Explanations of Implied Social Biases EMNLP 2023 BLIND: Bias Removal With No Demographics ACL 2023 BotPercent: Estimating Bot Populations in Twitter Communities EMNLP 2023 Bridging the Digital Divide: Performance Variation across Socio-Economic Factors in Vision-Language Models EMNLP 2023 Bugs in the Data: How ImageNet Misrepresents Biodiversity AAAI 2023 Building Socio-culturally Inclusive Stereotype Resources with Community Engagement NeurIPS 2023 Building Stereotype Repositories with Complementary Approaches for Scale and Depth EACL 2023 But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI AISTATS 2023 Can We Trust Fair-AI? AAAI 2023 Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data NeurIPS 2023 CAST: Conditional Attribute Subsampling Toolkit for Fine-Grained Evaluation WACV 2023 Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness NeurIPS 2023 Causal-Debias: Unifying Debiasing in Pretrained Language Models and Fine-tuning via Causal Invariant Learning ACL 2023 Causal Fairness for Outcome Control NeurIPS 2023 Causal Intervention for Mitigating Name Bias in Machine Reading Comprehension ACL 2023 Censored Fairness through Awareness AAAI 2023 Centering the Margins: Outlier-Based Identification of Harmed Populations in Toxicity Detection EMNLP 2023 CertiFair: A Framework for Certified Global Fairness of Neural Networks AAAI 2023 Certification of Distributional Individual Fairness NeurIPS 2023 Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation ICLR 2023 Certified Robust Neural Networks: Generalization and Corruption Resistance ICML 2023 Certifying Fairness of Probabilistic Circuits AAAI 2023