Further reading

To learn more about the topics that were covered in this chapter, please take a look at the following references:

• Algorithmia. (2021). 2021 Enterprise Trends in Machine Learning. Seattle: Algorithmia.
• Mayer-Schönberger, V. and Cukier, K. (2013). Big Data: A Revolution That Will Transform How We Live, Work, and Think. Boston/New York: Eamon Dolan/Houghton Mifflin Harcourt.
• The Economist. (2010, February 27). Data, data everywhere. The Economist.
• Data Privacy Manager. (2021, October 1). Data Privacy vs. Data Security [definitions and comparisons]. Data Privacy Manager.
• IBM. (2021). Cost of a Data Breach Report 2021. New York: IBM.
• Burgess, M. (2020, March 24). What is GDPR? The summary guide to GDPR compliance in the UK. Wired.
• TrustArc. (2021). Global Privacy Benchmarks Survey 2021. Walnut Creek: TrustArc.
• Auxier, B., Rainie, L., Anderson, M., Perrin, A., Kumar, M. and Turner, E. (2019, November 15). Americans and Privacy: Concerned, Confused and Feeling Lack of Control Over Their Personal Information. Pew Research Center.
• Hes, R. and Borking, J. (1995). Privacy-Enhancing Technologies: The Path to Anonymity. Hague: Information and Privacy Commissioner of Ontario.
• Goldsteen, A., Ezov, G., Shmelkin, R., Moffie, M. and Farkash, A. (2021). Data minimization for GDPR Compliance in machine learning models. AI and Ethics, 1-15.
• Knight, W. (2019, November 19). The Apple Card Didn’t ‘See’ Gender—and That’s the Problem. Wired.
• Gebru, T. and Denton, E. (2020). Tutorial on Fairness Accountability Transparency and Ethics in Computer Vision at CVPR 2020. Available online at https://sites.google.com/view/fatecv-tutorial/home.
• Ukanwa, K. (2021, May 3). Algorithmic bias isn’t just unfair — it’s bad for business. The Boston Globe.
• O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Crown.
• Blackman, R. (2020, October 15). A Practical Guide to Building Ethical AI. Harvard Business Review.
• Ginsberg, J., Mohebbi, M., Patel, R., Brammer, L., Smolinski, M. S. and Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature 457, 1012–1014.
• Anderson, C. (2008, June 23). The End of Theory: The Data Deluge Makes the Scientific Method Obsolete. Wired.
• Butler, D. (2013). When Google got flu wrong. Nature 494, 155–156.
• Harford, T. (2014, March 28). Big data: are we making a big mistake?. Financial Times.
• Dral, E. and Samuylova, E. (2020, November 12). Machine Learning Monitoring, Part 5: Why You Should Care About Data and Concept Drift. Evidently AI Blog.
• Forrester Consulting. (2021). Deploy ML Models To In-Memory: Databases For Blazing Fast Performance. Retrieved from https://redis.com/wp-content/uploads/2021/06/forrester-ai-opportunity-snapshot.pdf.
• Sato, D., Wider, A. and Windheuser, C. (2019, September 19). Continuous Delivery for Machine Learning: Automating the end-to-end lifecycle of Machine Learning applications. Retrieved from martinFowler.com at https://martinfowler.com/articles/cd4ml.html.
• Verma, D. C. (2021). Federated AI for Real-World Business Scenarios. New York: CRC Press.
• Bostrom, R. P. and Heinen, J. S. (1977). MIS problems and failures: A socio-technical perspective. Part I: The causes. MIS Quarterly, 1(3), pp. 17.
• Weld, D. S., Lin, C. H. and Bragg, J. (2015). Artificial intelligence and collective intelligence. Handbook of Collective Intelligence, 89-114.
• Abay, A., Zhou, Y., Baracaldo, N., Rajamoni, S., Chuba, E. and Ludwig, H. Mitigating Bias in Federated Learning. Available at https://arxiv.org/pdf/2012.02447.pdf.
• Big Data: A Revolution That Will Transform How We Live, Work, and Think (https://www.amazon.com/Big-Data-Revolution-Transform-Think/dp/0544227751