Fairness

Certifying Robustness to Programmable Data Bias in Decision Trees NeurIPS 2021 Challenges in Automated Debiasing for Toxic Language Detection EACL 2021 Challenges in Detoxifying Language Models EMNLP 2021 Changing the World by Changing the Data ACL 2021 Characterizing Fairness Over the Set of Good Models Under Selective Labels ICML 2021 Characterizing the risk of fairwashing NeurIPS 2021 Classification with abstention but without disparities UAI 2021 Classification with Strategically Withheld Data AAAI 2021 CogTree: Cognition Tree Loss for Unbiased Scene Graph Generation IJCAI 2021 Collaborative Bayesian Optimization with Fair Regret ICML 2021 Combining Fairness and Optimality when Selecting and Allocating Projects IJCAI 2021 Competency Problems: On Finding and Removing Artifacts in Language Data EMNLP 2021 Conditionally independent data generation UAI 2021 Consistent Instance False Positive Improves Fairness in Face Recognition CVPR 2021 Constrained Optimization to Train Neural Networks on Critical and Under-Represented Classes NeurIPS 2021 Constructing a Fair Classifier with Generated Fair Data AAAI 2021 Constructing a Psychometric Testbed for Fair Natural Language Processing EMNLP 2021 Context-sensitive evaluation of automatic speech recognition: considering user experience & language variation EACL 2021 Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation AAAI 2021 Controlling Fairness and Bias in Dynamic Learning-to-Rank (Extended Abstract) IJCAI 2021 Counterfactual Explanations Can Be Manipulated NeurIPS 2021 Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder AAAI 2021 Counterfactual Inference for Text Classification Debiasing ACL 2021 Counterfactual Invariance to Spurious Correlations in Text Classification NeurIPS 2021 dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python JMLR 2021