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Fairness

3337 directly classified papers

Papers per year

Papers

Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection ACL 2020
Double-Hard Debias: Tailoring Word Embeddings for Gender Bias Mitigation ACL 2020
Generating Natural Counterfactual Visual Explanations IJCAI 2020
Detecting East Asian Prejudice on Social Media EMNLP 2020
A Novel Methodology for Developing Automatic Harassment Classifiers for Twitter EMNLP 2020
Collaborative Machine Learning with Incentive-Aware Model Rewards ICML 2020
In Data We Trust: A Critical Analysis of Hate Speech Detection Datasets EMNLP 2020
Fortifying Toxic Speech Detectors Against Veiled Toxicity EMNLP 2020
Investigating Annotator Bias with a Graph-Based Approach EMNLP 2020
Identifying and Measuring Annotator Bias Based on Annotators’ Demographic Characteristics EMNLP 2020
Six Attributes of Unhealthy Conversations EMNLP 2020
Bounding the fairness and accuracy of classifiers from population statistics ICML 2020
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing ICML 2020
HirePeer: Impartial Peer-Assessed Hiring at Scale in Expert Crowdsourcing Markets AAAI 2020
Demoting Racial Bias in Hate Speech Detection ACL 2020
Enhancing Bias Detection in Political News Using Pragmatic Presupposition ACL 2020
Contextualizing Hate Speech Classifiers with Post-hoc Explanation ACL 2020
Mitigating Gender Bias Amplification in Distribution by Posterior Regularization ACL 2020
Is Your Classifier Actually Biased? Measuring Fairness under Uncertainty with Bernstein Bounds ACL 2020
Fair Embedding Engine: A Library for Analyzing and Mitigating Gender Bias in Word Embeddings EMNLP 2020
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference NIPS 2020
FR-Train: A Mutual Information-Based Approach to Fair and Robust Training ICML 2020
Countering hate on social media: Large scale classification of hate and counter speech EMNLP 2020
Predictive Multiplicity in Classification ICML 2020
We Can Detect Your Bias: Predicting the Political Ideology of News Articles EMNLP 2020