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Past research has demonstrated that the explicit use of protected attributes in machine learning can improve both performance and fairness.
Encoding Categorical Variables with Conjugate Bayesian Models for WeWork Lead Scoring Engine
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Coding categorical variables
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Boosted decision trees as an alternative to artificial neural networks for particle identification
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An empirical comparison of supervised learning algorithms
Caruana, R. and Niculescu-Mizil, A. (2006) · 2006
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Discrimination-aware data mining
Pedreschi, D., Ruggieri, S., and Turini, F. (2008) · 2008
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Aleatory or epistemic? does it matter?
Der Kiureghian, A. and Ditlevsen, O. (2009) · 2009
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Aleatory or epistemic? does it matter?
Kiureghian, A. D. and Ditlevsen, O. (2009) · 2009
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Data mining for discrimination discovery
Ruggieri, S., Pedreschi, D., and Turini, F. (2010) · 2010
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Data preprocessing techniques for classification without discrimination
Kamiran, F. and Calders, T. (2011) · 2011
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Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011) · 2011
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Regression for Categorical Data
Tutz, G. (2011) · 2011
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Fairness through awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R. S. (2012) · 2012
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Charter of fundamental rights of the european union
European Commission (2012) · 2012
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Fairness-aware classifier with prejudice remover regularizer
Kamishima, T., Akaho, S., Asoh, H., and Sakuma, J. (2012) · 2012
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Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics
Crenshaw, K. (2013) · 2013
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Learning fair representations
Zemel, R. S., Wu, Y., Swersky, K., Pitassi, T., and Dwork, C. (2013) · 2013
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Big data: Seizing opportunities and preserving values
Podesta, J., Pritzker, P., Moniz, E. J., Holden, J., and Zients, J. (2014) · 2014
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A multidisciplinary survey on discrimination analysis
Romei, A. and Ruggieri, S. (2014) · 2014
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Certifying and removing disparate impact
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Introductory Econometrics: A Modern Approach
Wooldridge, J. M. (2015) · 2015
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A gradient boosting method to improve travel time prediction
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Big data’s disparate impact
Barocas, S. and Selbst, A. D. (2016) · 2016
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Xgboost: A scalable tree boosting system
Chen, T. and Guestrin, C. (2016) · 2016
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Uncertainty in Deep Learning
Gal, Y. (2016) · 2016
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Equality of opportunity in supervised learning
Hardt, M., Price, E., and Srebro, N. (2016a) · 2016
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Equality of opportunity in supervised learning
Hardt, M., Price, E., and Srebro, N. (2016b) · 2016
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Big data: A report on algorithmic systems, opportunity, and civil right
Munoz, C., Smith, M., and Patil, D. (2016) · 2016
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"why should I trust you?": Explaining the predictions of any classifier
A general approach to fairness with optimal transport
Chiappa, S., Jiang, R., Stepleton, T., Pacchiano, A., Jiang, H., and Aslanides, J. (2020) · 2020
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Bayesian modeling of intersectional fairness: The variance of bias
Foulds, J. R., Islam, R., Keya, K. N., and Pan, S. (2020a) · 2020
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Catboost for big data: an interdisciplinary review
Hancock, J. T. and Khoshgoftaar, T. M. (2020) · 2020
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Pots: protective optimization technologies
Kulynych, B., Overdorf, R., Troncoso, C., and Gürses, S. F. (2020) · 2020
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Bias preservation in machine learning: the legality of fairness metrics under eu non-discrimination law
Wachter, S., Mittelstadt, B., and Russell, C. (2020) · 2020
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Category encoders :a library of sklearn compatible categorical variable encoders
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Ribeiro, M. T., Singh, S., and Guestrin, C. (2016) · 2016
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Using sensitive personal data may be necessary for avoiding discrimination in data-driven decision models
Zliobaite, I. and Custers, B. (2016) · 2016
Cited alongside, same era.
Algorithmic decision making and the cost of fairness
Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., and Huq, A. (2017) · 2017
Cited alongside, same era.
Fairness constraints: Mechanisms for fair classification
Zafar, M. B., Valera, I., Gomez-Rodriguez, M., and Gummadi, K. P. (2017b) · 2017
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From parity to preference-based notions of fairness in classification
Zafar, M. B., Valera, I., Gomez-Rodriguez, M., Gummadi, K. P., and Weller, A. (2017c) · 2017
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Similarity encoding for learning with dirty categorical variables
Cerda, P., Varoquaux, G., and Kégl, B. (2018) · 2018
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Will McGinnis (2020) · 2020
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Tuning eu equality law to algorithmic discrimination: Three pathways to resilience
Xenidis, R. (2020) · 2020
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Exploring racial bias within face recognition via per-subject adversarially-enabled data augmentation
Yucer, S., Akçay, S., Moubayed, N. A., and Breckon, T. P. (2020) · 2020
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Deep neural networks and tabular data: A survey
Borisov, V., Leemann, T., Seßler, K., Haug, J., Pawelczyk, M., and Kasneci, G. (2021) · 2021
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Retiring adult: New datasets for fair machine learning
Ding, F., Hardt, M., Miller, J., and Schmidt, L. (2021) · 2021
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Bridging machine learning and mechanism design towards algorithmic fairness
Finocchiaro, J., Maio, R., Monachou, F., Patro, G. K., Raghavan, M., Stoica, A.-A., and Tsirtsis, S. (2021) · 2021
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How we analyzed the compas recidivism algorithm
Jeff Larson, Surya Mattu, L. K. and Angwin, J. (2016) · 2021
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Quantile encoder: Tackling high cardinality categorical features in regression problems
Mougan, C., Masip, D., Nin, J., and Pujol, O. (2021b) · 2021
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Causal intersectionality and fair ranking
Yang, K., Loftus, J. R., and Stoyanovich, J. (2021) · 2021
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Tratamiento de variables categóricas en modelos de machine learning
Barragán, R. K. (2022) · 2022
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Encoding high-cardinality string categorical variables
Cerda, P. and Varoquaux, G. (2022) · 2022
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Sktools:tools to extend sklearn, feature engineering based transformers
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Why do tree-based models still outperform deep learning on tabular data?
Grinsztajn, L., Oyallon, E., and Varoquaux, G. (2022) · 2022
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A survey on bias and fairness in machine learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A. (2022) · 2022
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Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features
Pargent, F., Pfisterer, F., Thomas, J., and Bischl, B. (2022) · 2022
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Algorithmic tools in public employment services: Towards a jobseeker-centric perspective
Scott, K. M., Wang, S. M., Miceli, M., Delobelle, P., Sztandar-Sztanderska, K., and Berendt, B. (2022) · 2022
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Cesammo: Categorical encoding by statistical applied multivariable modeling
Valdez-Valenzuela, E., Kuri-Morales, A., and Gomez-Adorno, H. (2022) · 2022
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Towards intersectionality in machine learning: Including more identities, handling underrepresentation, and performing evaluation
Wang, A., Ramaswamy, V. V., and Russakovsky, O. (2022) · 2022
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Counterfactual situation testing: Uncovering discrimination under fairness given the difference
Álvarez, J. M. and Ruggieri, S. (2023) · 2023
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Multi-dimensional discrimination in law and machine learning - A comparative overview
Roy, A., Horstmann, J., and Ntoutsi, E. (2023) · 2023
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Can we trust fair-ai?
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