Barocas, Solon, and Andrew D. Selbst. 2014. Big Data’s Disparate Impact.” Working Paper Series. SSRN eLibrary.
Bolukbasi, Tolga, Kai-Wei Chang, James Y. Zou, Venkatesh Saligrama, and Adam Tauman Kalai. 2016. “Man Is to Computer Programmer as Woman Is to Homemaker? Debiasing Word Embeddings.” ArXiv abs/1607.06520.
Breiman, Leo. 2001. “Random Forests.” Machine Learning 45 (1): 5–32.
Buolamwini, Joy, and Timnit Gebru. 2018. “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification.” In Proceedings of the 1st Conference on Fairness, Accountability and Transparency, edited by Sorelle A. Friedler and Christo Wilson, 81:77–91. Proceedings of Machine Learning Research. New York, NY, USA: PMLR.
Carrillo, Cantú, A. 2020. “Individual Explanations in Machine.” IADB.
Chawla, Nitesh V., Kevin W. Bowyer, Lawrence O. Hall, and W. Philip Kegelmeyer. 2002. “SMOTE: Synthetic Minority over-Sampling Technique.” Journal of Artificial Intelligence Research 16: 321–57.
Cristina Pombo, Natalia González Alarcón, Marcelo Cabrol. 2020. “fAIr LAC: Adopción Ética y Responsable de La Inteligencia Artificial En América Latina y El Caribe.” Nota Técnica No. IDB-TN-1839.
DrivenData. 2019. “An Ethics Checklist for Data Scientists.”
Friedman, Jerome H. 2001. “Greedy Function Approximation: A Gradient Boosting Machine.” The Annals of Statistics 29 (5): 1189–1232.
Fritzler, A. 2015. “An Ethical Checklist for Data Sciencie.”
Gebru, Timnit, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III au2, and Kate Crawford. 2020. “Datasheets for Datasets.”
Gelman, Andrew, and Jennifer Hill. 2006. Data Analysis Using Regression and Multilevel/Hierarchical Models. 1st ed. Cambridge University Press.
Greene, W. H. 2003. Econometric Analysis. Pearson Education.
Hardt, Moritz, Eric Price, and Nathan Srebro. 2016. “Equality of Opportunity in Supervised Learning.” CoRR abs/1610.02413.
Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2017. The Elements of Statistical Learning. Springer Series in Statistics. Springer New York Inc.
Hvitfeldt, Emil. 2020. Themis: Extra Recipes Steps for Dealing with Unbalanced Data.
Imai, Kosuke, Gary King, and Elizabeth Stuart. 2008. “Misunderstandings Among Experimentalists and Observationalists about Causal Inference.” Journal of the Royal Statistical Society, Series A 171, part 2: 481502.
INEGI. 2014. “Encuesta Nacional de Ingresos y Gastos de Los Hogares (ENIGH-2014). Diseño Muestral.”
James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to Statistical Learning: With Applications in r. Springer.
Jobin, Anna, Marcello Ienca, and Effy Vayena. 2019. “The Global Landscape of AI Ethics Guidelines.” Nature Machine Intelligence.
Kaufman, Shachar, Saharon Rosset, and Claudia Perlich. 2011. “Leakage in Data Mining: Formulation, Detection, and Avoidance.” In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 6:556–63.
Kuhn, Max. 2020. Tune: Tidy Tuning Tools.
Kuhn, Max, Fanny Chow, and Hadley Wickham. 2020. Rsample: General Resampling Infrastructure.
Kuhn, Max, and Davis Vaughan. 2020a. Parsnip: A Common API to Modeling and Analysis Functions.
———. 2020b. Yardstick: Tidy Characterizations of Model Performance.
Kuhn, Max, and Hadley Wickham. 2020. Recipes: Preprocessing Tools to Create Design Matrices.
Kuhn, M., and K. Johnson. 2013. Applied Predictive Modeling. SpringerLink : Bücher. Springer New York.
Lackland, Daniel. 2014. “Racial Differences in Hypertension: Implications for High Blood Pressure Management.” The American Journal of the Medical Sciences 348 (June).
Little, R. J. A., and D. B. Rubin. 2002. Statistical Analysis with Missing Data. Wiley Series in Probability and Mathematical Statistics. Probability and Mathematical Statistics. Wiley.
Lohr, S. L. 2009. Sampling: Design and Analysis. Advanced (Cengage Learning). Cengage Learning.
Lundberg, Scott M, and Su-In Lee. 2017. “A Unified Approach to Interpreting Model Predictions.” In Advances in Neural Information Processing Systems 30, edited by I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, 4765–74. Curran Associates, Inc.
Lundberg, Scott, and Su-In Lee. 2017. “A Unified Approach to Interpreting Model Predictions.” ArXiv abs/1705.07874.
Mellon, Jonathan, and Christopher Prosser. 2017. “Twitter and Facebook Are Not Representative of the General Population: Political Attitudes and Demographics of British Social Media Users.” Research & Politics 4 (3): 2053168017720008.
Miller, Tim. 2019. “Explanation in Artificial Intelligence: Insights from the Social Sciences.” Artif. Intell. 267: 1–38.
Mitchell, Margaret, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2018. “Model Cards for Model Reporting.” CoRR abs/1810.03993.
Molnar, Christoph. 2019. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable.
Obermeyer, Ziad, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. 2019. “Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations.” Science 366 (6464): 447–53.
OECD. 2019c. Artificial Intelligence in Society.
———. 2021. Https://
Pedersen, Thomas Lin. 2019. Patchwork: The Composer of Plots.
Rossouw, Jacques E., Johan du Plessis, A J Spinnler Benadé, P C Jordaan, Johan Kotze, Pieter L. Jooste, and José Joaquim Ferreira. 1983. “Coronary Risk Factor Screening in Three Rural Communities. The CORIS Baseline Study.” South African Medical Journal = Suid-Afrikaanse Tydskrif Vir Geneeskunde 64 12: 430–36.
Sundararajan, Mukund, Ankur Taly, and Qiqi Yan. 2017. “Axiomatic Attribution for Deep Networks.” ArXiv abs/1703.01365.
Suresh, Harini, and John V. Guttag. 2019. “A Framework for Understanding Unintended Consequences of Machine Learning.” ArXiv abs/1901.10002.
van Buuren, Stef, and Karin Groothuis-Oudshoorn. 2011. mice: Multivariate Imputation by Chained Equations in r.” Journal of Statistical Software 45 (3): 1–67.
Vaughan, Davis. 2020. Workflows: Modeling Workflows.
Vaver, Jon, and Jim Koehler. 2011. “Measuring Ad Effectiveness Using Geo Experiments.” Google Inc.
Verma, Sahil, and Julia Rubin. 2018. “Fairness Definitions Explained.” In Proceedings of the International Workshop on Software Fairness, 1–7. FairWare ’18. New York, NY, USA: Association for Computing Machinery.
Wachter, Sandra, Brent D. Mittelstadt, and Chris Russell. 2017. “Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR.” ArXiv abs/1711.00399.
Wager, Stefan, and Susan Athey. 2018. “Estimation and Inference of Heterogeneous Treatment Effects Using Random Forests.” Journal of the American Statistical Association.
Wickham, Hadley. 2017. Tidyverse: Easily Install and Load the ’Tidyverse’.
Williams, D M. 1981. Racial differences of hemoglobin concentration: measurements of iron, copper, and zinc.” The American Journal of Clinical Nutrition 34 (9): 1694–1700.
Wilson, Jacque. 2014. “What Your IQ Score Doesn’t Tell You.” CNN.
Xie, Yihui. 2019. Knitr: A General-Purpose Package for Dynamic Report Generation in r.