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Collecting more diverse and representative training data is often touted as a remedy for the disparate performance of machine learning predictors across subpopulations.
On the two different aspects of the representative method: The method of stratified sampling and the method of purposive selection
Neyman, J · 1934
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SMOTE: Synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P · 2002
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Theory of classification: A survey of some recent advances
Boucheron, S., Bousquet, O., and Lugosi, G · 2005
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Optimal design of experiments
Pukelsheim, F · 2006
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Power-law distributions in empirical data
Clauset, A., Shalizi, C. R., and Newman, M. E · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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Sampling: design and analysis
Lohr, S. L · 2009
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Learning bounds for importance weighting
Cortes, C., Mansour, Y., and Mohri, M · 2010
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Mechanism design for fair division: Allocating divisible items without payments
Cole, R., Gkatzelis, V., and Goel, G · 2013
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HarvardX Person-Course Academic Year 2013 De-Identified dataset, version 3.0, 2014
HarvardX · 2014
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The fair guiding principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al · 2016
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Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)
Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., Kalloo, A., Liopyris, K., Mishra, N. K., Kittler, H., and Halpern, A · 2017
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UCI machine learning repository, 2017
Dua, D. and Graff, C · 2017
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Learning from class-imbalanced data: Review of methods and applications
Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H., and Bing, G · 2017
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Inclusivefacenet: Improving face attribute detection with race and gender diversity
Ryu, H. J., Adam, H., and Mitchell, M · 2017
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Shankar, S., Halpern, Y., Breck, E., Atwood, J., Wilson, J., and Sculley, D · 2017
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A systematic study of the class imbalance problem in convolutional neural networks
Buda, M., Maki, A., and Mazurowski, M. A · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Buolamwini, J. and Gebru, T · 2018
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Why is my classifier discriminatory?
Chen, I. Y., Johansson, F. D., and Sontag, D. A · 2018
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Decoupled classifiers for group-fair and efficient machine learning
Dwork, C., Immorlica, N., Kalai, A. T., and Leiserson, M · 2018
Cited alongside, same era.
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., and Crawford, K · 2018
Cited alongside, same era.
Fairness without demographics in repeated loss minimization
Multiaccuracy: Black-box post-processing for fairness in classification
Kim, M. P., Ghorbani, A., and Zou, J · 2019
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Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial ai products
Raji, I. D. and Buolamwini, J · 2019
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A framework for understanding unintended consequences of machine learning
Suresh, H. and Guttag, J. V · 2019
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Who’s responsible? Jointly quantifying the contribution of the learning algorithm and training data
Yona, G., Ghorbani, A., and Zou, J · 2019
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Adaptive sampling to reduce disparate performance
Abernethy, J., Awasthi, P., Kleindessner, M., Morgenstern, J., and Zhang, J · 2020
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Hashimoto, T. B., Srivastava, M., Namkoong, H., and Liang, P · 2018
Cited alongside, same era.
Does distributionally robust supervised learning give robust classifiers?
Hu, W., Niu, G., Sato, I., and Sugiyama, M · 2018
Cited alongside, same era.
Dealing with bias via data augmentation in supervised learning scenarios
Iosifidis, V. and Ntoutsi, E · 2018
Cited alongside, same era.
The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Tschandl, P., Rosendahl, C., and Kittler, H · 2018
Cited alongside, same era.
Are all training examples created equal? An empirical study
Vodrahalli, K., Li, K., and Malik, J · 2018
Cited alongside, same era.
Item recommendation on monotonic behavior chains
Wan, M. and McAuley, J. J · 2018
Cited alongside, same era.
What is the effect of importance weighting in deep learning?
Byrd, J. and Lipton, Z. C · 2019
Cited alongside, same era.
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Bringing the people back in: Contesting benchmark machine learning datasets
Denton, E., Hanna, A., Amironesei, R., Smart, A., Nicole, H., and Scheuerman, M. K · 2020
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Value-laden disciplinary shifts in machine learning
Dotan, R. and Milli, S · 2020
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Lessons from archives: Strategies for collecting sociocultural data in machine learning
Gebru, T · 2020
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Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem
Hofmanninger, J., Prayer, F., Pan, J., Röhrich, S., Prosch, H., and Langs, G · 2020
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Data and its (dis) contents: A survey of dataset development and use in machine learning research
Paullada, A., Raji, I. D., Bender, E. M., Denton, E., and Hanna, A · 2020
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Distributionally robust neural networks
Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P · 2020
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No subclass left behind: Fine-grained robustness in coarse-grained classification problems
Sohoni, N., Dunnmon, J., Angus, G., Gu, A., and Ré, C · 2020
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A general exact optimal sample allocation algorithm: With bounded cost and bounded sample sizes
Wright, T · 2020
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Towards fairer datasets: Filtering and balancing the distribution of the people subtree in the imagenet hierarchy
Yang, K., Qinami, K., Fei-Fei, L., Deng, J., and Russakovsky, O · 2020
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Narratives and counternarratives on data sharing in africa
Abebe, R., Aruleba, K., Birhane, A., Kingsley, S., Obaido, G., Remy, S. L., and Sadagopan, S · 2021
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Representativeness in statistics, politics, and machine learning
Chasalow, K. and Levy, K · 2021
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