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In domains ranging from computer vision to natural language processing, machine learning models have been shown to exhibit stark disparities, often performing worse for members of traditionally underserved groups.
Active Sampling for Min-Max Fairness
Jacob Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Chris Russell, and Jie Zhang. 2021 · 2006
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Race, ethnicity, culture, and disparities in health care
Leonard E Egede. 2006 · 2006
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Three naive bayes approaches for discrimination-free classification
Toon Calders and Sicco Verwer. 2010 · 2010
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The Next PAGE in Understanding Complex Traits: Design for the Analysis of Population Architecture Using Genetics and Epidemiology (PAGE) Study
Tara C Matise, Jose Luis Ambite, Steven Buyske, Christopher S Carlson, Shelley A Cole, Dana C Crawford, Christopher A Haiman, Gerardo Heiss, Charles Kooperberg, Loic Le Marchand, et al · 2011
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Fairness through awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference . 214–226
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. 2012 · 2012
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Fairness-aware classifier with prejudice remover regularizer. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Springer, 35–50
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma. 2012 · 2012
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Quantifying explainable discrimination and removing illegal discrimination in automated decision making
Faisal Kamiran, Indrė Žliobaitė, and Toon Calders. 2013 · 2013
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Discrimination aware classification for imbalanced datasets. In Proceedings of the 22nd ACM international conference on Information & Knowledge Management . 1529–1532
Goce Ristanoski, Wei Liu, and James Bailey. 2013 · 2013
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Demographic Dialectal Variation in Social Media: A Case Study of African-American English. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing . 1119–1130
Su Lin Blodgett, Lisa Green, and Brendan O’Connor. 2016 · 2016
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A Confidence-Based Approach for Balancing Fairness and Accuracy. In Proceedings of the 2016 SIAM International Conference on Data Mining . SIAM, 144–152
Benjamin Fish, Jeremy Kun, and Ádám D Lelkes. 2016 · 2016
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Equality of Opportunity in Supervised Learning
Moritz Hardt, Eric Price, and Nati Srebro. 2016 · 2016
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Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes
Jerome Kelleher, Alison M Etheridge, and Gilean McVean. 2016 · 2016
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Genomics is failing on diversity
Alice B Popejoy and Stephanie M Fullerton. 2016 · 2016
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A convex framework for fair regression
Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth. 2017 · 2017
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Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J Bryson, and Arvind Narayanan. 2017 · 2017
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova. 2017 · 2017
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Algorithmic decision making and the cost of fairness. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . 797–806
Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq. 2017 · 2017
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Inherent Trade-Offs in the Fair Determination of Risk Scores. In 8th Innovations in Theoretical Computer Science Conference (ITCS 2017) . 43:1–43:23
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. 2017 · 2017
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Counterfactual Fairness. In Proceedings of the 31st International Conference on Neural Information Processing Systems
Matt Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. 2017 · 2017
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Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations
Alicia R Martin, Christopher R Gignoux, Raymond K Walters, Genevieve L Wojcik, Benjamin M Neale, Simon Gravel, Mark J Daly, Carlos D Bustamante, and Eimear E Kenny. 2017 · 2017
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Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science
EM Bender and Batya Friedman. 2018 · 2018
Cited alongside, same era.
Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In Conference on Fairness, Accountability and Transparency . PMLR, 77–91
Joy Buolamwini and Timnit Gebru. 2018 · 2018
Cited alongside, same era.
The measure and mismeasure of fairness: A critical review of fair machine learning
Sam Corbett-Davies and Sharad Goel. 2018 · 2018
Cited alongside, same era.
Polygenic risk scores: a biased prediction?
Francisco M De La Vega and Carlos D Bustamante. 2018 · 2018
Cited alongside, same era.
Machine Learning, Health Disparities, and Causal Reasoning
Steven N Goodman, Sharad Goel, and Mark R Cullen. 2018 · 2018
Cited alongside, same era.
Decennial Census
US Census Bureau. 2020 · 2020
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Fair allocation through selective information acquisition. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society . 22–28
William Cai, Johann Gaebler, Nikhil Garg, and Sharad Goel. 2020 · 2020
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Counterfactual risk assessments, evaluation, and fairness. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency . 582–593
Amanda Coston, Alan Mishler, Edward H Kennedy, and Alexandra Chouldechova. 2020 · 2020
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The Dataset Nutrition Label
Sarah Holland, Ahmed Hosny, and Sarah Newman. 2020 · 2020
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Jeremy Irvin, Hao Sheng, Neel Ramachandran, Sonja Johnson-Yu, Sharon Zhou, Kyle Story, Rose Rustowicz, Cooper Elsworth, Kemen Austin, and Andrew Y Ng. 2020 · 2020
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Michael Hind, Sameep Mehta, Aleksandra Mojsilovic, Ravi Nair, Karthikeyan Natesan Ramamurthy, Alexandra Olteanu, and Kush R Varshney. 2018 · 2018
Cited alongside, same era.
Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations
Amit V Khera, Mark Chaffin, Krishna G Aragam, Mary E Haas, Carolina Roselli, Seung Hoan Choi, Pradeep Natarajan, Eric S Lander, Steven A Lubitz, Patrick T Ellinor, et al · 2018
Cited alongside, same era.
Fairness definitions explained. In 2018 IEEE/ACM International Workshop on Software Fairness (FairWare) . IEEE, 1–7
Sahil Verma and Julia Rubin. 2018 · 2018
Cited alongside, same era.
High-level Expert Group on Artificial Intelligence
HLEG AI. 2019 · 2019
Cited alongside, same era.
On fairness in budget-constrained decision making. In KDD Workshop of Explainable Artificial Intelligence
Michiel A Bakker, Alejandro Noriega-Campero, Duy Patrick Tu, Prasanna Sattigeri, Kush R Varshney, and AS Pentland. 2019 · 2019
Cited alongside, same era.
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting. In Proceedings of the Conference on Fairness, Accountability, and Transparency . 120–128
Maria De-Arteaga, Alexey Romanov, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, and Adam Tauman Kalai. 2019 · 2019
Cited alongside, same era.
A scientometric review of genome-wide association studies
Melinda C Mills and Charles Rahal. 2019 · 2019
Cited alongside, same era.
Racial disparities in automated speech recognition
Allison Koenecke, Andrew Nam, Emily Lake, Joe Nudell, Minnie Quartey, Zion Mengesha, Connor Toups, John R Rickford, Dan Jurafsky, and Sharad Goel. 2020 · 2020
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Spatio-Temporal Deep Learning Approach to Map Deforestation in Amazon Rainforest
Raian V Maretto, Leila MG Fonseca, Nathan Jacobs, Thales S Körting, Hugo N Bendini, and Leandro L Parente. 2020 · 2020
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AViD Dataset: Anonymized Videos from Diverse Countries. In Advances in Neural Information Processing Systems (NeurIPS)
AJ Piergiovanni and Michael S. Ryoo. 2020 · 2020
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Promoting fairness in learned models by learning to active learn under parity constraints. In Workshop on Real World Experiment Design and Active Learning. International Conference on Machine Learning
Amr Sharaf and Hal Daumé III. 2020 · 2020
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Proposal for a regulation of the European Parliament and the Council laying down harmonised rules on Artificial Intelligence (Artificial Intelligence Act) and amending certain Union legislative acts
Artificial Intelligence Act. 2021 · 2021
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Deep neural network improves the estimation of polygenic risk scores for breast cancer
Adrien Badré, Li Zhang, Wellington Muchero, Justin C Reynolds, and Chongle Pan. 2021 · 2021
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Can Active Learning Preemptively Mitigate Fairness Issues?
Frédéric Branchaud-Charron, Parmida Atighehchian, Pau Rodríguez, Grace Abuhamad, and Alexandre Lacoste. 2021 · 2021
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Inclusion of variants discovered from diverse populations improves polygenic risk score transferability
Taylor B Cavazos and John S Witte. 2021 · 2021
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Learning to be Fair: A Consequentialist Approach to Equitable Decision-Making
Alex Chohlas-Wood, Madison Coots, Emma Brunskill, and Sharad Goel. 2021 · 2021
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The People’s Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage
Daniel Galvez, Greg Diamos, Juan Manuel Ciro Torres, Keith Achorn, Anjali Gopi, David Kanter, Max Lam, Mark Mazumder, and Vijay Janapa Reddi. 2021 · 2021
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Datasheets for datasets
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé Iii, and Kate Crawford. 2021 · 2021
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Towards Measuring Fairness in AI: the Casual Conversations Dataset
Caner Hazirbas, Joanna Bitton, Brian Dolhansky, Jacqueline Pan, Albert Gordo, and Cristian Canton Ferrer. 2021 · 2021
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Fairness in Risk Assessment Instruments: Post-Processing to Achieve Counterfactual Equalized Odds. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency . 386–400
Alan Mishler, Edward H Kennedy, and Alexandra Chouldechova. 2021 · 2021
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Causal Conceptions of Fairness and their Consequences
Hamed Nilforoshan, Johann Gaebler, Ravi Shroff, and Sharad Goel. 2022 · 2022
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