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The growing capability and accessibility of machine learning has led to its application to many real-world domains and data about people.
Algorithm as 136: A k-means clustering algorithm
J. A. Hartigan and M. A. Wong · 1979
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k-means++: The advantages of careful seeding
D. Arthur and S. Vassilvitskii · 2007
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Clustering cancer gene expression data: a comparative study
M. C. de Souto, I. G. Costa, D. S. de Araujo, T. B. Ludermir, and A. Schliep · 2008
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Application of k means clustering algorithm for prediction of students academic performance
O. Oyelade, O. Oladipupo, and I. Obagbuwa · 2010
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Gestalt: integrated support for implementation and analysis in machine learning
K. Patel, N. Bancroft, S. M. Drucker, J. Fogarty, A. J. Ko, and J. Landay · 2010
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R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork · 2013
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Modeltracker: Redesigning performance analysis tools for machine learning
S. Amershi, M. Chickering, S. M. Drucker, B. Lee, P. Simard, and J. Suh · 2015
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Machine bias
J. Angwin, J. Larson, L. Kirchner, and S. Mattu · 2016
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Big data’s disparate impact
S. Barocas and A. D. Selbst · 2016
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Compas risk scales: Demonstrating accuracy equity and predictive parity
W. Dieterich, C. Mendoza, and T. Brennan · 2016
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On the (im) possibility of fairness
S. A. Friedler, C. Scheidegger, and S. Venkatasubramanian · 2016
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Equality of opportunity in supervised learning
M. Hardt, E. Price, N. Srebro, et al · 2016
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Visual exploration of machine learning results using data cube analysis
M. Kahng, D. Fang, and D. H. Chau · 2016
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
A. Chouldechova · 2017
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UCI machine learning repository
D. Dheeru and E. Karra Taniskidou · 2017
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Inherent trade-offs in the fair determination of risk scores
J. M. Kleinberg, S. Mullainathan, and M. Raghavan · 2017
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A workflow for visual diagnostics of binary classifiers using instance-level explanations
J. Krause, A. Dasgupta, J. Swartz, Y. Aphinyanaphongs, and E. Bertini · 2017
Cited alongside, same era.
A user study on the effect of aggregating explanations for interpreting machine learning models
J. Krause, A. Perer, and E. Bertini · 2018
Later among the works it cites.
Seq2Seq-Vis: A visual debugging tool for sequence-to-sequence models
H. Strobelt, S. Gehrmann, M. Behrisch, A. Perer, H. Pfister, and A. M. Rush · 2018
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Fairgan: Fairness-aware generative adversarial networks
D. Xu, S. Yuan, L. Zhang, and X. Wu · 2018
Later among the works it cites.
Putting fairness principles into practice: Challenges, metrics, and improvements
A. Beutel, J. Chen, T. Doshi, H. Qian, A. Woodruff, C. Luu, P. Kreitmann, J. Bischof, and E. H. Chi · 2019
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Slice finder: Automated data slicing for model validation
Y. Chung, T. Kraska, N. Polyzotis, K. Tae, and S. E. Whang · 2019
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What if tool
Google · 2019
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Counterfactual fairness
M. J. Kusner, J. Loftus, C. Russell, and R. Silva · 2017
Cited alongside, same era.
Squares: Supporting interactive performance analysis for multiclass classifiers
D. Ren, S. Amershi, B. Lee, J. Suh, and J. D. Williams · 2017
Cited alongside, same era.
Counterfactual explanations without opening the black box: automated decisions and the gdpr
S. Wachter, B. Mittelstadt, and C. Russell · 2017
Cited alongside, same era.
ActiVis: Visual exploration of industry-scale deep neural network models
M. Kahng, P. Y. Andrews, A. Kalro, and D. H. Chau · 2018
Cited alongside, same era.
Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
M. Kearns, S. Neel, A. Roth, and Z. S. Wu · 2018
Cited alongside, same era.
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Visual analytics in deep learning: An interrogative survey for the next frontiers
F. Hohman, M. Kahng, R. Pienta, and D. H. Chau · 2019
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Improving fairness in machine learning systems: What do industry practitioners need?
K. Holstein, J. Wortman Vaughan, H. Daumé, III, M. Dudik, and H. Wallach · 2019
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COMPAS recidivism risk score data and analysis
ProPublica · 2019
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Predictive inequity in object detection
B. Wilson, J. Hoffman, and J. Morgenstern · 2019
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