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Although the fairness community has recognized the importance of data, researchers in the area primarily rely on UCI Adult when it comes to tabular data.
Uci adult data set
R. Kohavi and B. Becker · 1996
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Discrimination-aware data mining
D. Pedreschi, S. Ruggieri, and F. Turini · 2008
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Building classifiers with independency constraints
T. Calders, F. Kamiran, and M. Pechenizkiy · 2009
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The changing science of machine learning, 2011
P. Langley · 2011
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Unbiased look at dataset bias
A. Torralba and A. A. Efros · 2011
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Learning fair representations
R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork · 2013
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The black box society
F. Pasquale · 2015
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Big data’s disparate impact
S. Barocas and A. D. Selbst · 2016
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
T. Bolukbasi, K.-W. Chang, J. Y. Zou, V. Saligrama, and A. T. Kalai · 2016
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Equality of opportunity in supervised learning
M. Hardt, E. Price, and N. Srebro · 2016
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The point of collection
M. Onuoha · 2016
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Semantics derived automatically from language corpora contain human-like biases
A. Caliskan, J. J. Bryson, and A. Narayanan · 2017
Cited alongside, same era.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
A. Chouldechova · 2017
Cited alongside, same era.
A reductions approach to fair classification
A. Agarwal, A. Beygelzimer, M. Dudík, J. Langford, and H. Wallach · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
J. Buolamwini and T. Gebru · 2018
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Automating inequality: How high-tech tools profile, police, and punish the poor
V. Eubanks · 2018
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Race after Technology
R. Benjamin · 2019
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Ghost work: how to stop Silicon Valley from building a new global underclass
M. L. Gray and S. Suri · 2019
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Fairlearn: A toolkit for assessing and improving fairness in ai
S. Bird, M. Dudík, R. Edgar, B. Horn, R. Lutz, V. Milan, M. Sameki, H. Wallach, and K. Walker · 2020
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Integrated Public Use Microdata Series, Current Population Survey: Version 8.0 [dataset], 2020
S. Flood, M. King, R. Rodgers, S. Ruggles, and J. R. Warren · 2020
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Lessons from archives: strategies for collecting sociocultural data in machine learning
E. S. Jo and T. Gebru · 2020
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Data and its (dis) contents: A survey of dataset development and use in machine learning research
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T. Gebru, J. Morgenstern, B. Vecchione, J. W. Vaughan, H. Wallach, H. Daumé III, and K. Crawford · 2018
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How copyright law can fix artificial intelligence’s implicit bias problem
A. Levendowski · 2018
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Mitigating bias in artificial intelligence (ai) models – ibm research, Feb 2019
R. Puri · 2018
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M. Arjovsky, L. Bottou, I. Gulrajani, and D. Lopez-Paz · 2019
Cited alongside, same era.
Fairness and Machine Learning
S. Barocas, M. Hardt, and A. Narayanan · 2019
Cited alongside, same era.
Ai fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias
R. K. Bellamy, K. Dey, M. Hind, S. C. Hoffman, S. Houde, K. Kannan, P. Lohia, J. Martino, S. Mehta, A. Mojsilović, et al · 2019
Cited alongside, same era.
A. Paullada, I. D. Raji, E. M. Bender, E. Denton, and A. Hanna · 2020
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Large image datasets: A pyrrhic win for computer vision?
V. U. Prabhu and A. Birhane · 2020
Later among the works it cites.
Towards fairer datasets: Filtering and balancing the distribution of the people subtree in the imagenet hierarchy
K. Yang, K. Qinami, L. Fei-Fei, J. Deng, and O. Russakovsky · 2020
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Narratives and counternarratives on data sharing in africa
R. Abebe, K. Aruleba, A. Birhane, S. Kingsley, G. Obaido, S. L. Remy, and S. Sadagopan · 2021
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It’s compaslicated: The messy relationship between rai datasets and algorithmic fairness benchmarks
M. Bao, A. Zhou, S. Zottola, B. Brubach, S. Desmarais, A. Horowitz, K. Lum, and S. Venkatasubramanian · 2021
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Patterns, predictions, and actions: A story about machine learning
M. Hardt and B. Recht · 2021
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