References

“Algoritmos de Inteligência Artificial E Vieses: Uma Reflexão Sobre ética E Justiça.” 2020. https://www.programaria.org/algoritmos-de-inteligencia-artificial-e-vieses-uma-reflexao-sobre-etica-e-justica/.

Apley, Daniel W., and Jingyu Zhu. 2016. “Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models.”

Breiman, Leo. 2001. “Random Forests.” Machine Learning 45 (1). Springer: 5–32.

Doshi-Velez, e Been Kim, Finale. 2017. “Towards a rigorous science of interpretable machine learning.” arXiv Preprints arXiv:1702.08608.

Friedman, Jerome H. 2001. “Greedy Function Approximation: A Gradient Boosting Machine.” The Annals of Statistics 29 (5). Institute of Mathematical Statistics: 1189–1232. http://www.jstor.org/stable/2699986.

Goldstein, Alex, Adam Kapelner, Justin Bleich, and Emil Pitkin. 2015. “Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation.” Journal of Computational and Graphical Statistics 24 (1): 44–65. https://doi.org/10.1080/10618600.2014.907095.

Guardian, The. 2018. “Amazon ditched AI recruiting tool that favored men for technical jobs.” \url{https://www.theguardian.com/technology/2018/oct/10/amazon-hiring-ai-gender-bias-recruiting-engine}.

Lapuschkin, S., S. Waldchen, A. Binder, G. Montavon, W. Samek, and K. Muller. 2019. “Unmasking Clever Hans Predictors and Assessing What Machines Really Learn.” Nature Communications.

Liu, Weibo, Zidong Wang, Xiaohui Liu, Nianyin Zeng, Yurong Liu, and Fuad E Alsaadi. 2017. “A Survey of Deep Neural Network Architectures and Their Applications.” Neurocomputing 234. Elsevier: 11–26.

Molnar, Christoph. 2019. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. Online.

O’Neil, Cathy. 2016. Weapons of Math Destruction. United States: Crown Books.

Reuters. 2018. “Amazon scraps secret AI recruiting tool that showed bias against women.” \url{https://www.reuters.com/article/us-amazon-com-jobs-automation-insight}.

Ribeiro, M., S. Singh, and C. Guestrin. 2016. “Model-Agnostic Interpretability of Machine Learning.” arXiv Preprint arXiv:1606.05386.

Shawe-Taylor, John, and Nello Cristianini. 2000. “Support Vector Machines.” An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, 93–112.