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

24 October 2016

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

50 / 207 papers shown

Title
Algorithmic Fairness in Education René F. Kizilcec Hansol Lee FaML 25 119 0 10 Jul 2020
Fair Performance Metric Elicitation G. Hiranandani Harikrishna Narasimhan Oluwasanmi Koyejo 19 18 0 23 Jun 2020
Two Simple Ways to Learn Individual Fairness Metrics from Data Debarghya Mukherjee Mikhail Yurochkin Moulinath Banerjee Yuekai Sun FaML 21 96 0 19 Jun 2020
Fair clustering via equitable group representations Mohsen Abbasi Aditya Bhaskara Suresh Venkatasubramanian FaML FedML 21 86 0 19 Jun 2020
Extending the Machine Learning Abstraction Boundary: A Complex Systems Approach to Incorporate Societal Context Donald Martin Vinodkumar Prabhakaran Jill A. Kuhlberg A. Smart William S. Isaac FaML 6 40 0 17 Jun 2020
Fairness in Forecasting and Learning Linear Dynamical Systems Quan-Gen Zhou Jakub Mareˇcek Robert Shorten AI4TS 11 7 0 12 Jun 2020
How Interpretable and Trustworthy are GAMs? C. Chang S. Tan Benjamin J. Lengerich Anna Goldenberg R. Caruana FAtt 6 76 0 11 Jun 2020
Principles to Practices for Responsible AI: Closing the Gap Daniel S. Schiff B. Rakova A. Ayesh Anat Fanti M. Lennon 19 87 0 08 Jun 2020
Participatory Problem Formulation for Fairer Machine Learning Through Community Based System Dynamics Donald Martin Vinodkumar Prabhakaran Jill A. Kuhlberg A. Smart William S. Isaac FaML 8 62 0 15 May 2020
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction Caroline Linjun Wang Bin Han Bhrij Patel Cynthia Rudin FaML HAI 57 83 0 08 May 2020
A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Set Philippe C. Besse E. del Barrio Paula Gordaliza Jean-Michel Loubes Laurent Risser FaML 6 62 0 31 Mar 2020
Auditing ML Models for Individual Bias and Unfairness Songkai Xue Mikhail Yurochkin Yuekai Sun MLAU 40 22 0 11 Mar 2020
Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation Raha Moraffah Mansooreh Karami Ruocheng Guo A. Raglin Huan Liu CML ELM XAI 11 212 0 09 Mar 2020
Trustworthy AI Jeannette M. Wing 12 213 0 14 Feb 2020
Joint Optimization of AI Fairness and Utility: A Human-Centered Approach Yunfeng Zhang Rachel K. E. Bellamy Kush R. Varshney 6 38 0 05 Feb 2020
Algorithmic Fairness Dana Pessach E. Shmueli FaML 22 387 0 21 Jan 2020
Leveraging Semi-Supervised Learning for Fairness using Neural Networks Vahid Noroozi S. Bahaadini Samira Sheikhi Nooshin Mojab Philip S. Yu 8 7 0 31 Dec 2019
Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics Debjani Saha Candice Schumann Duncan C. McElfresh John P. Dickerson Michelle L. Mazurek Michael Carl Tschantz FaML 16 16 0 17 Dec 2019
Fair Data Adaptation with Quantile Preservation Drago Plečko N. Meinshausen 14 28 0 15 Nov 2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot S. Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 11 6,106 0 22 Oct 2019
Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability Christopher Frye C. Rowat Ilya Feige 14 179 0 14 Oct 2019
Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data Dylan Slack Sorelle A. Friedler Emile Givental FaML 19 54 0 24 Aug 2019
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 299 4,203 0 23 Aug 2019
With Malice Towards None: Assessing Uncertainty via Equalized Coverage Yaniv Romano Rina Foygel Barber C. Sabatti Emmanuel J. Candès UQCV 11 73 0 15 Aug 2019
A Causal Bayesian Networks Viewpoint on Fairness Silvia Chiappa William S. Isaac FaML 12 62 0 15 Jul 2019
Does Object Recognition Work for Everyone? Terrance Devries Ishan Misra Changhan Wang L. V. D. van der Maaten 16 261 0 06 Jun 2019
Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination Nathan Kallus Xiaojie Mao Angela Zhou FaML 13 155 0 01 Jun 2019
Fair Regression: Quantitative Definitions and Reduction-based Algorithms Alekh Agarwal Miroslav Dudík Zhiwei Steven Wu FaML 16 240 0 30 May 2019
Average Individual Fairness: Algorithms, Generalization and Experiments Michael Kearns Aaron Roth Saeed Sharifi-Malvajerdi FaML FedML 9 84 0 25 May 2019
Learning Fair Representations via an Adversarial Framework Rui Feng Yang Yang Yuehan Lyu Chenhao Tan Yizhou Sun Chunping Wang FaML 11 55 0 30 Apr 2019
The invisible power of fairness. How machine learning shapes democracy E. Beretta A. Santangelo Bruno Lepri A. Vetrò Juan Carlos De Martin FaML 13 6 0 22 Mar 2019
Predictive Inequity in Object Detection Benjamin Wilson Judy Hoffman Jamie Morgenstern 19 218 0 21 Feb 2019
Fair Decisions Despite Imperfect Predictions Niki Kilbertus Manuel Gomez Rodriguez Bernhard Schölkopf Krikamol Muandet Isabel Valera FaML OffRL 13 5 0 08 Feb 2019
Equal Opportunity in Online Classification with Partial Feedback Yahav Bechavod Katrina Ligett Aaron Roth Bo Waggoner Zhiwei Steven Wu FaML 11 60 0 06 Feb 2019
Fair and Unbiased Algorithmic Decision Making: Current State and Future Challenges Songül Tolan FaML 16 31 0 15 Jan 2019
Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements Alex Beutel Jilin Chen Tulsee Doshi Hai Qian Allison Woodruff Christine Luu Pierre Kreitmann Jonathan Bischof Ed H. Chi FaML 26 150 0 14 Jan 2019
Crowdsourcing with Fairness, Diversity and Budget Constraints Naman Goel Boi Faltings FaML 8 20 0 31 Oct 2018
Model Cards for Model Reporting Margaret Mitchell Simone Wu Andrew Zaldivar Parker Barnes Lucy Vasserman Ben Hutchinson Elena Spitzer Inioluwa Deborah Raji Timnit Gebru 31 1,831 0 05 Oct 2018
From Soft Classifiers to Hard Decisions: How fair can we be? R. Canetti A. Cohen Nishanth Dikkala Govind Ramnarayan Sarah Scheffler Adam D. Smith FaML 6 59 0 03 Oct 2018
Can everyday AI be ethical. Fairness of Machine Learning Algorithms Philippe C. Besse C. Castets-Renard Aurélien Garivier Jean-Michel Loubes FaML 11 5 0 03 Oct 2018
Correspondences between Privacy and Nondiscrimination: Why They Should Be Studied Together Anupam Datta S. Sen Michael Carl Tschantz 13 5 0 06 Aug 2018
Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees L. E. Celis Lingxiao Huang Vijay Keswani Nisheeth K. Vishnoi FaML 44 301 0 15 Jun 2018
What About Applied Fairness? Jared Sylvester Edward Raff FaML 14 10 0 13 Jun 2018
Multiaccuracy: Black-Box Post-Processing for Fairness in Classification Michael P. Kim Amirata Ghorbani James Y. Zou MLAU 12 335 0 31 May 2018
Causal Reasoning for Algorithmic Fairness Joshua R. Loftus Chris Russell Matt J. Kusner Ricardo M. A. Silva FaML CML 18 125 0 15 May 2018
Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems S. Kiritchenko Saif M. Mohammad FaML 11 430 0 11 May 2018
Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction Nina Grgic-Hlaca Elissa M. Redmiles Krishna P. Gummadi Adrian Weller FaML 6 225 0 26 Feb 2018
Path-Specific Counterfactual Fairness Silvia Chiappa Thomas P. S. Gillam CML FaML 19 334 0 22 Feb 2018
Online Learning with an Unknown Fairness Metric Stephen Gillen Christopher Jung Michael Kearns Aaron Roth FaML 14 143 0 20 Feb 2018
A comparative study of fairness-enhancing interventions in machine learning Sorelle A. Friedler C. Scheidegger Suresh Venkatasubramanian Sonam Choudhary Evan P. Hamilton Derek Roth FaML 21 634 0 13 Feb 2018