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Algorithmic fairness has emphasized the role of biased data in automated decision outcomes.
MUC-4 evaluation metrics
Nancy Chinchor · 1992
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Accountability in a Computerized Society
Helen Nissenbaum · 1996
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Breast cancer in men
Sharon H Giordano, Aman U Buzdar, and Gabriel N Hortobagyi · 2002
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The Elements of Statistical Learning: Data Mining, Inference and Prediction
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2009
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Learning multiple layers of features from tiny images, 2009
A Krizhevsky · 2009
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John R Zech, Jessica Zosa Forde, and Michael L Littman · 2009
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Practical bayesian optimization of machine learning algorithms
J Snoek, H Larochelle, and R P Adams · 2012
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The statistical crisis in science: data-dependent analysis—a “garden of forking paths”–explains why many statistically significant comparisons don’t hold up
Andrew Gelman and Eric Loken · 2014
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ImageNet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C Berg, and Li Fei-Fei · 2015
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with deep learning, November 2017
Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Ding, Aarti Bagul, Curtis Langlotz, Katie Shpanskaya, Matthew P Lungren, and Andrew Y Ng · 2017
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ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
X Wang, Y Peng, L Lu, Z Lu, M Bagheri, and R M Summers · 2017
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The Marginal Value of Adaptive Gradient Methods in Machine Learning
Ashia C Wilson, Rebecca Roelofs, Mitchell Stern, Nati Srebro, and Benjamin Recht · 2017
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Machine Learning and Health Care Disparities in Dermatology
Adewole S Adamson and Avery Smith · 2018
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Sanity Checks for Saliency Maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
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Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
Joy Buolamwini and Timnit Gebru · 2018
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Decoupled Classifiers for Group-Fair and Efficient Machine Learning
Cynthia Dwork, Nicole Immorlica, Adam Tauman Kalai, and Max Leiserson · 2018
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Deep Reinforcement Learning That Matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2018
Selective brain damage: Measuring the disparate impact of model pruning, November 2019
Sara Hooker, Aaron Courville, Yann Dauphin, and Andrea Frome · 2019
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Measurement and Fairness, December 2019
Abigail Z Jacobs and Hanna Wallach · 2019
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Problem Formulation and Fairness
Samir Passi and Solon Barocas · 2019
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Mathematical notions vs. human perception of fairness: A descriptive approach to fairness for machine learning
Megha Srivastava, Hoda Heidari, and Andreas Krause · 2019
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Troubleshooting deep neural networks, January 2019
Josh Tobin · 2019
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Roles for Computing in Social Change
Rediet Abebe, Solon Barocas, Jon Kleinberg, Karen Levy, Manish Raghavan, and David G. Robinson · 2020
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Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov · 2019
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Unreproducible Research is Reproducible
Xavier Bouthillier, César Laurent, and Pascal Vincent · 2019
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On Empirical Comparisons of Optimizers for Deep Learning, 2019
D Choi, C J Shallue, Z Nado, J Lee, and others · 2019
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Does object recognition work for everyone?
Terrance de Vries, Ishan Misra, Changhan Wang, and Laurens van der Maaten · 2019
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The RSNA Pediatric Bone Age Machine Learning Challenge
Safwan S Halabi, Luciano M Prevedello, Jayashree Kalpathy-Cramer, Artem B Mamonov, Alexander Bilbily, Mark Cicero, Ian Pan, Lucas Araújo Pereira, Rafael Teixeira Sousa, Nitamar Abdala, Felipe Campos Kitamura, Hans H Thodberg, Leon Chen, George Shih, Katherine Andriole, Marc D Kohli, Bradley J Erickson, and Adam E Flanders · 2019
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Hacking the Cis-tem
M. Hicks · 2019
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Bringing the people back in: Contesting benchmark machine learning datasets
Emily Denton, Alex Hanna, Razvan Amironesei, Andrew Smart, Hilary Nicole, and Morgan Klaus Scheuerman · 2020
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Optimizer Benchmarking Needs to Account for Hyperparameter Tuning
Prabhu Teja Sivaprasad, Florian Mai, Thijs Vogels, Martin Jaggi, and François Fleuret · 2020
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Emergent Unfairness: Normative Assumptions and Contradictions in Algorithmic Fairness-Accuracy Trade-Off Research, 2021
A. Feder Cooper and Ellen Abrams · 2021
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Hyperparameter Optimization Is Deceiving Us, and How to Stop It, 2021
A. Feder Cooper, Yucheng Lu, and Christopher De Sa · 2021
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CheXclusion: Fairness gaps in deep chest X-ray classifiers
L Seyyed-Kalantari, G Liu, M McDermott, and M Ghassemi · 2021
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