Making Artificial Intelligence Transparent: Fairness and the Problem of Proxy Variables

Bibliography

• Alpaydin, Ethem. Machine Learning: The New AI. Cambridge, MA: MIT Press, 2016.
   Google Scholar
• Arrieta, Alejandro Barredo, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, et al. “Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI.” arXiv.org. arXiv:1910.10045 [Cs.AI]. December 26, 2019. http://arxiv.org/abs/1910.10045.
   Google Scholar
• Bernard, Andreas. The Triumph of Profiling: The Self in Digital Culture. Translated by Valentine A. Pakis. Cambridge, MA: Polity Press, 2019.
   Google Scholar
• Bouk, Dan. How Our Days Became Numbered: Risk and the Rise of the Statistical Individual. Chicago: University of Chicago Press, 2018.
   Google Scholar
• Burkov, Andriy. The Hundred-Page Machine Learning Book. Andriy Burkov, 2019. https://www.onlineprogrammingbooks.com/the-hundred-page-machine-learning-book/
   Google Scholar
• Citron, Danielle, and Frank Pasquale. “The Scored Society: Due Process for Automated Predictions.” Washington Law Review 89, no. 1 (2014): 1–33.
   Web of Science ®Google Scholar
• Core, Mark G., H. Chad Lane, Michael Van Lent, Dave Gomboc, Steve Solomon, and Milton Rosenberg. “Building Explainable Artificial Intelligence Systems.” In Proceedings of the Association for Advancement of Artificial Intelligence, 1766–73. 2006, available at https://www.aaai.org/Library/AAAI/2006/aaai06-293.php
   Google Scholar
• Dwork, Cynthia, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. “Fairness Through Awareness.” Paper presented at the 3rd Innovations in Theoretical Computer Science Conference (ITCS), Cambridge, MA, January 8–10, 2012. https://doi.org/10.1145/2090236.2090255.
   Google Scholar
• Finlay, Steven. Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies. 2nd ed. Great Britain: Relativistic, 2017.
   Google Scholar
• Finlay, Steven. Artificial Intelligence for Everyone. Great Britain: Relativistic, 2020.
   Google Scholar
• Finlay, Steven. Predictive Analytics, Data Mining and Big Data: Myths, Misconceptions and Methods. New York, NY: Palgrave Macmillan, 2014.
   Google Scholar
• Finlay, Steven. Predictive Analytics in 56 Minutes. CreateSpace Independent Publishing Platform, 2015.
   Google Scholar
• Gunning, David, and David Aha. “DARPA’s Explainable Artificial Intelligence (XAI) Program.” AI Mag 40, no. 2 (June 24, 2019): 44–58. doi:10.1609/aimag.v40i2.2850.
   Web of Science ®Google Scholar
• Igo, Sarah E. The Averaged American: Surveys, Citizens, and the Making of a Mass Public. Cambridge, MA: Harvard University Press, 2007.
   Google Scholar
• Jacobs, Abigail Z., and Hanna Wallach. “Measurement and Fairness.” arXiv.org. arXiv:1912.05511 [Cs.CY], December 11, 2019. http://arxiv.org/abs/1912.05511.
   Google Scholar
• Kroll, Joshua A., Johnna Huey, Solon Barocas, Edward W. Felten, David G Robinson, Joel R. Reidenberg, and Harlan Yu. “Accountable Algorithms.” Univ PA Law Rev 165 (2017): 633.
   Web of Science ®Google Scholar
• Lauer, Josh. Creditworthy: A History of Consumer Surveillance and Financial Identity in America. New York: Columbia University Press, 2017.
   Google Scholar
• LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. “Deep Learning.” Nature 521, no. 7553 (May 2015): 436–444. doi:10.1038/nature14539.
   PubMed Web of Science ®Google Scholar
• Mau, Steffen. The Metric Society: On the Quantification of the Social. Medford, MA: Polity Press, 2019.
   Google Scholar
• Muller, Jerry. The Tyranny of Metrics. Princeton: Princeton University Press, 2018.
   Google Scholar
• O’Neil, Cathy. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Broadway Books, 2016.
   Google Scholar
• Roemer, John E. Equality of Opportunity. Cambridge, MA: Harvard University Press, 2000.
   Google Scholar
• Rule, James B. Privacy in Peril. Oxford: Oxford University Press, 2007.
   Google Scholar
• Ryu, Jae Yong, Hyun Uk Kim and Sang Yup Lee, “Deep Learning Improves Prediction of Drug–Drug and Drug–Food Interactions.” Proc Natl Acad Sci U S A 115, No. 18, (May 2018): 304–311.
   Web of Science ®Google Scholar
• Samek, Wojciech, Thomas Wiegand, and Klaus-Robert Müller. “Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models.” arXiv.org. arXiv:1708.08296 [Cs.AI], 2017. https://arxiv.org/abs/1708.08296
   Google Scholar
• Sloan, Robert H, and Richard Warner. “Beyond bias: Artificial Intelligence and Social Justice.” Va J Law Technol 24, no. 1 (2020): 1.
   Google Scholar
• Sloan, Robert H, and Richard Warner. The Privacy Fix: How to Preserve Privacy in the Onslaught of Surveillance. Cambridge: Cambridge University Press, forthcoming 2021
   Google Scholar
• Wickens, Michael R. “A Note on the Use of Proxy Variables.” Econometrica 40, no. 4 (1972): 759–761.
   Web of Science ®Google Scholar