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Fairness

796 directly classified papers

Papers per year

Papers

Robustness via Curvature Regularization, and Vice Versa CVPR 2019
Learning Not to Learn: Training Deep Neural Networks With Biased Data CVPR 2019
Posing Fair Generalization Tasks for Natural Language Inference IJCNLP 2019
Wasserstein Fair Classification UAI 2019
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding UAI 2019
AI in Recruiting. Multi-agent Systems Architecture for Ethical and Legal Auditing IJCAI 2019
Maximin-Aware Allocations of Indivisible Goods IJCAI 2019
Fairness Towards Groups of Agents in the Allocation of Indivisible Items IJCAI 2019
Strategyproof and Approximately Maxmin Fair Share Allocation of Chores IJCAI 2019
Fair Allocation of Indivisible Goods and Chores IJCAI 2019
Weighted Maxmin Fair Share Allocation of Indivisible Chores IJCAI 2019
Portioning Using Ordinal Preferences: Fairness and Efficiency IJCAI 2019
Compositional Fairness Constraints for Graph Embeddings ICML 2019
Women’s Syntactic Resilience and Men’s Grammatical Luck: Gender-Bias in Part-of-Speech Tagging and Dependency Parsing ACL 2019
The many dimensions of algorithmic fairness in educational applications ACL 2019
Evaluating the Underlying Gender Bias in Contextualized Word Embeddings ACL 2019
The Role of Protected Class Word Lists in Bias Identification of Contextualized Word Representations ACL 2019
Good Secretaries, Bad Truck Drivers? Occupational Gender Stereotypes in Sentiment Analysis ACL 2019
Exploring Algorithmic Fairness in Robust Graph Covering Problems NIPS 2019
Assessing Social and Intersectional Biases in Contextualized Word Representations NIPS 2019
PC-Fairness: A Unified Framework for Measuring Causality-based Fairness NIPS 2019
On Making Stochastic Classifiers Deterministic NIPS 2019
The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric NIPS 2019
Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness NIPS 2019
All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously JMLR 2019