Papers citing 'Fair prediction with disparate impact: A study of bias in recidivism prediction instruments'

Title
Hard Choices in Artificial Intelligence: Addressing Normative Uncertainty through Sociotechnical CommitmentsAAAI/ACM Conference on AI, Ethics, and Society (AIES), 2019 Roel Dobbe T. Gilbert Yonatan Dov Mintz 138 19 0 20 Nov 2019
Fair Data Adaptation with Quantile Preservation Drago Plečko N. Meinshausen 130 35 0 15 Nov 2019
What Do Compressed Deep Neural Networks Forget? Sara Hooker Aaron Courville Gregory Clark Yann N. Dauphin Andrea Frome 217 199 0 13 Nov 2019
Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic FairnessPattern Recognition (Pattern Recognit.), 2019 Zhu Li Adrián Pérez-Suay Gustau Camps-Valls Dino Sejdinovic FaML 134 23 0 11 Nov 2019
A Human-in-the-loop Framework to Construct Context-aware Mathematical Notions of Outcome FairnessAAAI/ACM Conference on AI, Ethics, and Society (AIES), 2019 Mohammad Yaghini A. Krause Hoda Heidari FaML 160 23 0 08 Nov 2019
Learning Fair and Interpretable Representations via Linear Orthogonalization Yuzi He Keith Burghardt Kristina Lerman FaML 129 5 0 28 Oct 2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIInformation Fusion (Inf. Fusion), 2019 Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 650 7,150 0 22 Oct 2019
Optimization Hierarchy for Fair Statistical Decision ProblemsAnnals of Statistics (Ann. Stat.), 2019 A. Aswani Matt Olfat 291 3 0 18 Oct 2019
Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing Sanghamitra Dutta Dennis L. Wei Hazar Yueksel Pin-Yu Chen Sijia Liu Kush R. Varshney FaML 106 11 0 17 Oct 2019
Conditional Learning of Fair RepresentationsInternational Conference on Learning Representations (ICLR), 2019 Han Zhao Amanda Coston T. Adel Geoffrey J. Gordon FaML 172 120 0 16 Oct 2019
Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainabilityNeural Information Processing Systems (NeurIPS), 2019 Christopher Frye C. Rowat Ilya Feige 227 205 0 14 Oct 2019
Keeping Designers in the Loop: Communicating Inherent Algorithmic Trade-offs Across Multiple Objectives Bowen Yu Ye Yuan Loren G. Terveen Zhiwei Steven Wu Jodi Forlizzi Haiyi Zhu 136 2 0 07 Oct 2019
Group-based Fair Learning Leads to Counter-intuitive Predictions Ofir Nachum Heinrich Jiang FaML 85 2 0 04 Oct 2019
Generating Fair Universal Representations using Adversarial ModelsIEEE Transactions on Information Forensics and Security (IEEE TIFS), 2019 Peter Kairouz Jiachun Liao Chong Huang Maunil R. Vyas Monica Welfert Lalitha Sankar 346 18 0 27 Sep 2019
This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology D. Mulligan Joshua A. Kroll Nitin Kohli Richmond Y. Wong 136 77 0 26 Sep 2019
Fair-by-design explainable models for prediction of recidivism Eduardo Soares Plamen Angelov FaML 92 24 0 18 Sep 2019
Advancing subgroup fairness via sleeping expertsInformation Technology Convergence and Services (ITCS), 2019 Avrim Blum Thodoris Lykouris FedML 97 38 0 18 Sep 2019
A Distributed Fair Machine Learning Framework with Private Demographic Data ProtectionIndustrial Conference on Data Mining (IDM), 2019 Hui Hu Yijun Liu Zhen Wang Chao Lan FaML FedML 121 28 0 17 Sep 2019
Predictive Multiplicity in ClassificationInternational Conference on Machine Learning (ICML), 2019 Charles Marx Flavio du Pin Calmon Berk Ustun 314 169 0 14 Sep 2019
Learning Fair Rule Lists Ulrich Aïvodji Julien Ferry Sébastien Gambs Marie-José Huguet Mohamed Siala FaML 144 11 0 09 Sep 2019
Optimizing Generalized Rate Metrics through Game Equilibrium Harikrishna Narasimhan Andrew Cotter Maya R. Gupta 85 4 0 06 Sep 2019
Quantifying Infra-Marginality and Its Trade-off with Group Fairness Arpita Biswas Siddharth Barman Amit Deshpande Amit Sharma 53 3 0 03 Sep 2019
Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data Dylan Slack Sorelle A. Friedler Emile Givental FaML 176 56 0 24 Aug 2019
A Survey on Bias and Fairness in Machine LearningACM Computing Surveys (ACM CSUR), 2019 Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 1.0K 5,008 0 23 Aug 2019
Data Management for Causal Algorithmic FairnessIEEE Data Engineering Bulletin (DEB), 2019 Babak Salimi B. Howe Dan Suciu CML FaML 168 27 0 20 Aug 2019
Towards Reducing Biases in Combining Multiple Experts OnlineInternational Joint Conference on Artificial Intelligence (IJCAI), 2019 Yi Sun Iván Díaz Alfredo Cuesta-Infante K. Veeramachaneni FaML 124 0 0 19 Aug 2019
Tackling Algorithmic Bias in Neural-Network Classifiers using Wasserstein-2 RegularizationJournal of Mathematical Imaging and Vision (JMIV), 2019 Laurent Risser Alberto González Sanz Quentin Vincenot Jean-Michel Loubes 263 22 0 15 Aug 2019
With Malice Towards None: Assessing Uncertainty via Equalized Coverage Yaniv Romano Rina Foygel Barber C. Sabatti Emmanuel J. Candès UQCV 244 66 0 15 Aug 2019
Fair quantile regression Dana Yang John D. Lafferty D. Pollard 69 6 0 19 Jul 2019
A Causal Bayesian Networks Viewpoint on Fairness Silvia Chiappa William S. Isaac FaML 134 66 0 15 Jul 2019
Counterfactual Reasoning for Fair Clinical Risk PredictionMachine Learning in Health Care (MLHC), 2019 Stephen Pfohl Tony Duan D. Ding N. Shah OOD CML 117 63 0 14 Jul 2019
Fair Kernel Regression via Fair Feature Embedding in Kernel SpaceIEEE International Conference on Tools with Artificial Intelligence (ICTAI), 2019 Austin Okray Hui Hu Chao Lan FaML 117 4 0 04 Jul 2019
Operationalizing Individual Fairness with Pairwise Fair RepresentationsProceedings of the VLDB Endowment (PVLDB), 2019 Preethi Lahoti Krishna P. Gummadi Gerhard Weikum FaML 191 112 0 02 Jul 2019
The Sensitivity of Counterfactual Fairness to Unmeasured ConfoundingConference on Uncertainty in Artificial Intelligence (UAI), 2019 Niki Kilbertus Philip J. Ball Matt J. Kusner Adrian Weller Ricardo M. A. Silva 197 59 0 01 Jul 2019
Training individually fair ML models with Sensitive Subspace RobustnessInternational Conference on Learning Representations (ICLR), 2019 Mikhail Yurochkin Amanda Bower Yuekai Sun FaML OOD 160 122 0 28 Jun 2019
Learning Fair Representations for Kernel ModelsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2019 Zilong Tan Samuel Yeom Matt Fredrikson Ameet Talwalkar FaML 186 27 0 27 Jun 2019
Fairness criteria through the lens of directed acyclic graphical models Benjamin R. Baer Daniel E. Gilbert M. Wells FaML 88 7 0 26 Jun 2019
Age and gender bias in pedestrian detection algorithms Martim Brandao 102 51 0 25 Jun 2019
FlipTest: Fairness Testing via Optimal Transport Emily Black Samuel Yeom Matt Fredrikson 247 100 0 21 Jun 2019
Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices Manish Raghavan Solon Barocas Jon M. Kleinberg K. Levy MLAU FaML 197 615 0 21 Jun 2019
Inherent Tradeoffs in Learning Fair RepresentationsNeural Information Processing Systems (NeurIPS), 2019 Han Zhao Geoffrey J. Gordon FaML 351 229 0 19 Jun 2019
The Price of Local Fairness in Multistage SelectionInternational Joint Conference on Artificial Intelligence (IJCAI), 2019 V. Emelianov G. Arvanitakis Nicolas Gast Krishna P. Gummadi Patrick Loiseau 97 18 0 15 Jun 2019
Principled Frameworks for Evaluating Ethics in NLP Systems Shrimai Prabhumoye Elijah Mayfield A. Black 68 7 0 14 Jun 2019
Understanding artificial intelligence ethics and safetySocial Science Research Network (SSRN), 2019 David Leslie FaML AI4TS 114 412 0 11 Jun 2019
ProPublica's COMPAS Data Revisited M. Barenstein FaML 149 52 0 11 Jun 2019
Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination PatternsAAAI Conference on Artificial Intelligence (AAAI), 2019 YooJung Choi G. Farnadi Behrouz Babaki Karen Ullrich FaML 110 29 0 10 Jun 2019
Does Object Recognition Work for Everyone? Terrance Devries Ishan Misra Changhan Wang Laurens van der Maaten 188 281 0 06 Jun 2019
Near Neighbor: Who is the Fairest of Them All?Neural Information Processing Systems (NeurIPS), 2019 Sariel Har-Peled S. Mahabadi 123 22 0 06 Jun 2019
Assessing Disparate Impacts of Personalized Interventions: Identifiability and Bounds Nathan Kallus Angela Zhou 123 11 0 04 Jun 2019
Disparate Vulnerability to Membership Inference AttacksProceedings on Privacy Enhancing Technologies (PoPETs), 2019 B. Kulynych Mohammad Yaghini Giovanni Cherubin Michael Veale Carmela Troncoso 350 48 0 02 Jun 2019