{"list":"other","total":153,"rows":[{"id":"1602.05629","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","created":"2016-02-17","cited_by_count":7669},{"id":"1610.02413","title":"Equality of Opportunity in Supervised Learning","created":"2016-10-07","cited_by_count":2007},{"id":"1908.09635","title":"A Survey on Bias and Fairness in Machine Learning","created":"2019-08-23","cited_by_count":1917},{"id":"1708.05031","title":"Neural Collaborative Filtering","created":"2017-08-16","cited_by_count":1111},{"id":"1703.06856","title":"Counterfactual Fairness","created":"2017-03-20","cited_by_count":804},{"id":"1903.03894","title":"GNNExplainer: Generating Explanations for Graph Neural Networks","created":"2019-03-10","cited_by_count":737},{"id":"1412.3756","title":"Certifying and removing disparate impact","created":"2014-12-11","cited_by_count":715},{"id":"1610.07524","title":"Fair prediction with disparate impact: A study of bias in recidivism prediction instruments","created":"2016-10-24","cited_by_count":672},{"id":"1609.05807","title":"Inherent Trade-Offs in the Fair Determination of Risk Scores","created":"2016-09-19","cited_by_count":586},{"id":"1709.02012","title":"On Fairness and Calibration","created":"2017-09-06","cited_by_count":406},{"id":"1701.08230","title":"Algorithmic decision making and the cost of fairness","created":"2017-01-28","cited_by_count":403},{"id":"2203.13366","title":"Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)","created":"2022-03-24","cited_by_count":402},{"id":"2010.04053","title":"Fairness in Machine Learning: A Survey","created":"2020-10-04","cited_by_count":370},{"id":"1703.09207","title":"Fairness in Criminal Justice Risk Assessments: The State of the Art","created":"2017-03-27","cited_by_count":315},{"id":"1904.07451","title":"Counterfactual Visual Explanations","created":"2019-04-16","cited_by_count":313},{"id":"1602.05352","title":"Recommendations as Treatments: Debiasing Learning and Evaluation","created":"2016-02-17","cited_by_count":248},{"id":"2010.03240","title":"Bias and Debias in Recommender System: A Survey and Future Directions","created":"2020-10-07","cited_by_count":240},{"id":"2006.02587","title":"XGNN: Towards Model-Level Explanations of Graph Neural Networks","created":"2020-06-03","cited_by_count":211},{"id":"1804.11192","title":"Explainable Recommendation: A Survey and New Perspectives","created":"2018-04-30","cited_by_count":210},{"id":"1511.05897","title":"Censoring Representations with an Adversary","created":"2015-11-18","cited_by_count":192},{"id":"1802.07281","title":"Fairness of Exposure in Rankings","created":"2018-02-20","cited_by_count":190},{"id":"2101.06286","title":"Reinforcement learning based recommender systems: A survey","created":"2021-01-15","cited_by_count":188},{"id":"1802.08139","title":"Path-Specific Counterfactual Fairness","created":"2018-02-22","cited_by_count":181},{"id":"1808.06640","title":"Adversarial Removal of Demographic Attributes from Text Data","created":"2018-08-20","cited_by_count":170},{"id":"1707.00075","title":"Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations","created":"2017-07-01","cited_by_count":165},{"id":"2110.00530","title":"A survey on datasets for fairness-aware machine learning","created":"2021-10-01","cited_by_count":157},{"id":"1705.10378","title":"Fair Inference On Outcomes","created":"2017-05-29","cited_by_count":144},{"id":"1810.08810","title":"The Frontiers of Fairness in Machine Learning","created":"2018-10-20","cited_by_count":141},{"id":"1905.01989","title":"Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search","created":"2019-04-30","cited_by_count":138},{"id":"1705.11122","title":"Controllable Invariance through Adversarial Feature Learning","created":"2017-05-31","cited_by_count":127},{"id":"1903.00780","title":"Fairness in Recommendation Ranking through Pairwise Comparisons","created":"2019-03-02","cited_by_count":127},{"id":"1709.03221","title":"Fairness Testing: Testing Software for Discrimination","created":"2017-09-11","cited_by_count":126},{"id":"1905.10674","title":"Compositional Fairness Constraints for Graph Embeddings","created":"2019-05-25","cited_by_count":126},{"id":"1805.01788","title":"Equity of Attention: Amortizing Individual Fairness in Rankings","created":"2018-05-04","cited_by_count":124},{"id":"1908.08843","title":"Fairness in Deep Learning: A Computational Perspective","created":"2019-08-23","cited_by_count":123},{"id":"2102.03322","title":"CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks","created":"2021-02-05","cited_by_count":119},{"id":"1706.06368","title":"FA*IR: A Fair Top-k Ranking Algorithm","created":"2017-06-20","cited_by_count":113},{"id":"1906.05237","title":"Reinforcement Knowledge Graph Reasoning for Explainable Recommendation","created":"2019-06-12","cited_by_count":102},{"id":"2106.00467","title":"A Clarification of the Nuances in the Fairness Metrics Landscape","created":"2021-06-01","cited_by_count":98},{"id":"1611.03071","title":"Fairness in Reinforcement Learning","created":"2016-11-09","cited_by_count":95},{"id":"1909.04847","title":"RecSim: A Configurable Simulation Platform for Recommender Systems","created":"2019-09-11","cited_by_count":94},{"id":"1705.08804","title":"Beyond Parity: Fairness Objectives for Collaborative Filtering","created":"2017-05-24","cited_by_count":93},{"id":"1807.09685","title":"Grounding Visual Explanations","created":"2018-07-25","cited_by_count":93},{"id":"2007.13019","title":"Feedback Loop and Bias Amplification in Recommender Systems","created":"2020-07-25","cited_by_count":90},{"id":"1901.07555","title":"Managing Popularity Bias in Recommender Systems with Personalized Re-ranking","created":"2019-01-22","cited_by_count":82},{"id":"1706.07639","title":"Causal Embeddings for Recommendation","created":"2017-06-23","cited_by_count":79},{"id":"1901.09888","title":"Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System","created":"2019-01-29","cited_by_count":79},{"id":"2202.08816","title":"Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning","created":"2022-02-17","cited_by_count":79},{"id":"1704.06840","title":"Ranking with Fairness Constraints","created":"2017-04-22","cited_by_count":78},{"id":"2002.10764","title":"FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms","created":"2020-02-25","cited_by_count":78},{"id":"2101.03584","title":"Towards Long-term Fairness in Recommendation","created":"2021-01-10","cited_by_count":77},{"id":"2104.10671","title":"User-oriented Fairness in Recommendation","created":"2021-04-21","cited_by_count":75},{"id":"1902.04056","title":"Policy Learning for Fairness in Ranking","created":"2019-02-11","cited_by_count":74},{"id":"2006.02046","title":"Fairness-Aware Explainable Recommendation over Knowledge Graphs","created":"2020-06-03","cited_by_count":74},{"id":"1707.00093","title":"Multisided Fairness for Recommendation","created":"2017-07-01","cited_by_count":70},{"id":"1901.04562","title":"Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements","created":"2019-01-14","cited_by_count":70},{"id":"1907.13286","title":"The Unfairness of Popularity Bias in Recommendation","created":"2019-07-31","cited_by_count":70},{"id":"1606.07558","title":"Satisfying Real-world Goals with Dataset Constraints","created":"2016-06-24","cited_by_count":68},{"id":"2005.14713","title":"Controlling Fairness and Bias in Dynamic Learning-to-Rank","created":"2020-05-29","cited_by_count":68},{"id":"1910.12586","title":"PC-Fairness: A Unified Framework for Measuring Causality-based Fairness","created":"2019-10-20","cited_by_count":67},{"id":"1806.01059","title":"iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making","created":"2018-06-04","cited_by_count":64},{"id":"2104.05994","title":"Fairness in Rankings and Recommendations: An Overview","created":"2021-04-13","cited_by_count":62},{"id":"2010.11300","title":"How Do Fair Decisions Fare in Long-term Qualification?","created":"2020-10-21","cited_by_count":60},{"id":"2108.10539","title":"Counterfactual Explainable Recommendation","created":"2021-08-24","cited_by_count":60},{"id":"2105.11601","title":"Personalized Transformer for Explainable Recommendation","created":"2021-05-25","cited_by_count":59},{"id":"1906.05330","title":"Pairwise Fairness for Ranking and Regression","created":"2019-06-12","cited_by_count":58},{"id":"2201.03662","title":"Learning Fair Node Representations with Graph Counterfactual Fairness","created":"2022-01-10","cited_by_count":58},{"id":"1901.08568","title":"Algorithms for Fairness in Sequential Decision Making","created":"2019-01-24","cited_by_count":56},{"id":"1907.09013","title":"Conscientious Classification: A Data Scientist's Guide to Discrimination-Aware Classification","created":"2019-07-21","cited_by_count":55},{"id":"2010.09553","title":"Survey on Causal-based Machine Learning Fairness Notions","created":"2020-10-19","cited_by_count":54},{"id":"2004.13157","title":"Evaluating Stochastic Rankings with Expected Exposure","created":"2020-04-27","cited_by_count":53},{"id":"2203.12609","title":"Improving the Fairness of Chest X-ray Classifiers","created":"2022-03-23","cited_by_count":52},{"id":"2105.10648","title":"Deconfounded Recommendation for Alleviating Bias Amplification","created":"2021-05-22","cited_by_count":51},{"id":"2102.09140","title":"Learning Fair Representations for Recommendation: A Graph-based Perspective","created":"2021-02-18","cited_by_count":50},{"id":"1909.09141","title":"Causal Modeling for Fairness in Dynamical Systems","created":"2019-09-18","cited_by_count":47},{"id":"2006.16742","title":"FairRec: Fairness-aware News Recommendation with Decomposed Adversarial Learning","created":"2020-06-30","cited_by_count":44},{"id":"1712.09752","title":"Designing Fair Ranking Schemes","created":"2017-12-28","cited_by_count":40},{"id":"2008.09514","title":"Neural Logic Reasoning","created":"2020-08-20","cited_by_count":38},{"id":"2105.09829","title":"Personalized Counterfactual Fairness in Recommendation","created":"2021-05-20","cited_by_count":38},{"id":"1812.01504","title":"Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systems","created":"2018-12-02","cited_by_count":37},{"id":"2005.08129","title":"Neural Collaborative Reasoning","created":"2020-05-16","cited_by_count":37},{"id":"2001.04861","title":"Fairness in Learning-Based Sequential Decision Algorithms: A Survey","created":"2020-01-14","cited_by_count":35},{"id":"2008.09273","title":"The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation","created":"2020-08-21","cited_by_count":35},{"id":"2106.02705","title":"Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning","created":"2021-06-04","cited_by_count":34},{"id":"1805.08716","title":"Reducing Disparate Exposure in Ranking: A Learning To Rank Approach","created":"2018-05-22","cited_by_count":33},{"id":"1903.11719","title":"Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality","created":"2019-03-27","cited_by_count":33},{"id":"2103.14000","title":"Fairness in Ranking: A Survey","created":"2021-03-25","cited_by_count":33},{"id":"2104.09024","title":"TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers","created":"2021-04-19","cited_by_count":31},{"id":"2001.09773","title":"Algorithmic Fairness from a Non-ideal Perspective","created":"2020-01-08","cited_by_count":30},{"id":"1904.13341","title":"Learning Fair Representations via an Adversarial Framework","created":"2019-04-30","cited_by_count":29},{"id":"2105.05779","title":"Fairness in Information Access Systems","created":"2021-05-12","cited_by_count":29},{"id":"2201.00140","title":"Toward Pareto Efficient Fairness-Utility Trade-off inRecommendation through Reinforcement Learning","created":"2022-01-01","cited_by_count":29},{"id":"2204.08085","title":"CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems","created":"2022-04-17","cited_by_count":29},{"id":"1912.01799","title":"Addressing Marketing Bias in Product Recommendations","created":"2019-12-04","cited_by_count":23},{"id":"2109.04083","title":"User Tampering in Reinforcement Learning Recommender Systems","created":"2021-09-09","cited_by_count":23},{"id":"2110.06475","title":"SAR-Net: A Scenario-Aware Ranking Network for Personalized Fair Recommendation in Hundreds of Travel Scenarios","created":"2021-10-13","cited_by_count":23},{"id":"1707.01561","title":"Automatic Generation of Natural Language Explanations","created":"2017-07-04","cited_by_count":22},{"id":"2104.14527","title":"Online certification of preference-based fairness for personalized recommender systems","created":"2021-04-29","cited_by_count":22},{"id":"2205.00048","title":"Joint Multisided Exposure Fairness for Recommendation","created":"2022-04-29","cited_by_count":22},{"id":"1807.06349","title":"User Fairness in Recommender Systems","created":"2018-07-17","cited_by_count":21}],"next_cursor":"100"}