References
- Amrein-Beardsley, A. (2017). Breaking news: A big victory in court in Houston. Retrieved from http://vamboozled.com/breaking-news-victory-in-court-in-houston/
- Anonymous. (2018a). Government response to House of Lords Artificial Intelligence Select Committee’s Report on AI in the UK: Ready, Willing and Able? (CM9645). Her Majesty’s Stationery Office.
- Anonymous. (2018b). Initial code of conduct for data-driven health and care technology. Her Majesty’s Stationery Office Retrieved from https://www.gov.uk/government/publications/code-of-conduct-for-data-driven-health-and-care-technology/initial-code-of-conduct-for-data-driven-health-and-care-technology
- Ashford, W. (2018, June 29). AI is key to keeping IBM compliant with GDPR. Computer Weekly.
- Blunk, S. S., Clark, D. E., & McGibany, J. M. (2006). Evaluating the long-run impacts of the 9/11 terrorist attacks on US domestic airline travel. Applied Economics, 38(4), 363–370.
- Brockwell, P. J., & Davis, R. A. (2016). Introduction to time series and forecasting (3rd edn. ed.). Berlin: Springer.
- Brodbeck, F. C., Guillaume, Y. R. F., & Lee, N. J. (2011). Ethnic diversity as a multilevel construct: The combined effects of dissimilarity, group diversity, and societal status on learning performance in work groups. Journal of Cross-Cultural Psychology, 42(7), 1198–1218.
- Buffet, M. (2016). How do CDOs and CDSs influence the crisis of 2008. Lingnan Journal of Banking, Finance and Economics, 6(1).
- Burns, E. (2018). Tech experts weigh in on the AI hype cycle. Computer Weekly.
- Cambridge Dictionary. (2018). Bias. Retrieved from https://dictionary.cambridge.org/dictionary/english/bias.
- Candanedo, L. (2017). Appliances energy prediction data set. Retrieved from https://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction
- Chambers, M., & Dinsmore, T. W. (2015). Advanced analytics methodologies: Driving business value with analytics. Upper Saddle River, NJ: Pearson Education.
- Clark, L. (2018). AI elevates predictive maintenance for Kone and ThyssenKrupp. Computer Weekly.
- Clark, V., Reed, M., & Stephan, J. (2010). Using Monte Carlo simulation for a capital budgeting project. Management Accounting Quarterly, 12(1), 20–31.
- Cook, J. (2018, May 30). Amazon scraps ‘sexist AI’ recruiting tool that showed bias against women. Retrieved from https://www.telegraph.co.uk/technology/2018/10/10/amazon-scraps-sexist-ai-recruiting-tool-showed-bias-against/
- Edwards, J. S., & Finlay, P. N. (1997). Decision making with computers: The spreadsheet and beyond. London: Pitman.
- Edwards, J. S., & Rodriguez, E. (2016). Using knowledge management to give context to analytics and big data and reduce strategic risk. Procedia Computer Science, 99, 36–49.
- Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542, 115–118.
- European Spreadsheet Risks Interest Group. (2003). Spreadsheet mistakes - news stories collated by the European Spreadsheet Risks Interest Group. Retrieved from http://www.eusprig.org/horror-stories.htm.
- Fearn, N. (2018). Police facial recognition systems are 98 per cent inaccurate, says research. Retrieved from https://www.computing.co.uk/ctg/news/3032401/police-facial-recognition-systems-are-98-per-cent-inaccurate-says-research.
- Galton, F. (1907). Vox Populi. Nature, 75, 450–451.
- Gilliland, M., Tashman, L., & Sglavo, U. (Eds.). (2015). Business forecasting: Practical problems and solutions. Hoboken, NJ: John Wiley & Sons.
- Griffiths, D. F., & Higham, D. J. (2010). Numerical methods for ordinary differential equations: Initial value problems. London: Springer.
- Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). New Jersey: Pearson Prentice Hall.
- Hammond, J. S., Keeney, R. L., & Raiffa, H. (1998). The hidden traps in decision making. Harvard Business Review, 76(5), 47–56.
- Harris, T. (2016). How technology Hijacks People’s minds — From a magician and google’s design ethicist. Retrieved from http://www.tristanharris.com/essays/.
- Hayes, G. (2018). What does a data scientist REALLY look like? Retrieved from https://www.kdnuggets.com/2018/11/data-scientist-look-like.html
- Huber, G. P. (2004). The necessary nature of future firms: Attributes of survivors in a changing world. Thousand Oaks, CA: Sage Publications.
- Hyndman, R. J. (2018). A brief history of time series forecasting competitions. Retrieved from https://robjhyndman.com/hyndsight/forecasting-competitions/
- Jagadish, H. V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J. M., Ramakrishnan, R., & Shahabi, C. (2014). Exploring the inherent technical challenges in realizing the potential of big data. Communications of the ACM, 57(7), 86–94.
- Janis, I. L. (1982). Groupthink. Boston: Houghton Mifflin Company.
- Lebowitz, S., & Lee, S. (2015). 20 cognitive biases that screw up your decisions. Business Insider UK.
- Lin, N. (2014). Advanced business analytics: Integrating business process, big data, and advanced analytics. Upper Saddle River, NJ: Pearson Education.
- Martin, E. J. (2016). Dark data: Analyzing unused and ignored information. Econtent, 39(5), 6–8.
- McLean, B., & Elkind, P. (2003). The smartest guys in the room. New York: Portfolio.
- Moore, D. S., McCabe, G. P., & Craig, B. A. (2017). Introduction to the practice of statistics (9th ed.). New York: W. H. Freeman.
- O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. New York: Penguin Random House.
- Pidd, M. (1996). Tools for thinking: Modelling in management science. Chichester, England: John Wiley & Sons Ltd.
- Poston, T., & Stewart, I. (1978). Catastrophe theory and its applications. London: Pitman.
- Robinson, A., Levis, J., & Bennett, G. (2010). INFORMS to officially join analytics movement. OR/MS Today, 37(5), 59.
- Rosenhead, J. (1989). Rational analysis for a problematic world: Problem structuring methods for complexity, uncertainty and conflict. Chichester: John Wiley & Sons.
- Schiebinger, L. (2003). Women’s health and clinical trials. Journal of Clinical Investigation, 112(7), 973–977.
- Shortle, J. F., Thompson, J. M., Gross, D., & Harris, C. M. (2018). Fundamentals of queueing theory (5th ed.). Hoboken, NJ: John Wiley & Sons.
- Simon, H. A. (1969). The sciences of the artificial (1st ed.). Cambridge, MA: MIT Press.
- Simons, R., Mintzberg, H., & Basu, K. (2002, June). Memo to CEOs: The five half-truths of business. Fast Company, 59, 117–121.
- Snow, J. (2018). Amazon’s face recognition falsely matched 28 members of Congress with mugshots. Retrieved from https://www.aclu.org/blog/privacy-technology/surveillance-technologies/amazons-face-recognition-falsely-matched-28.
- Tao, A. L. (2018, July 30). How F1 and others are moving beyond descriptive analytics. Computer Weekly.
- Thibodeau, P. (2018, August 31). Gartner analyst sees limit to tech’s ability to fix hiring bias. Computer Weekly.
- Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.
- Tversky, A., & Kahneman, D. (1983). Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment. Psychological Review, 90(4), 293–315.
- Vicario, M. D., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., … Quattrociocchi, W. (2016). The spreading of misinformation online. Proceedings of the National Academy of Sciences USA, 113(3), 554–559. Retrieved from: https://www.pnas.org/content/pnas/early/2016/01/02/1517441113.full.pdf
- von der Beck, I., Cress, U., & Oeberst, A. (2018). Is there hindsight bias without real hindsight? Conjectures are sufficient to elicit hindsight bias. Journal of Experimental Psychology Applied. doi:10.1037/xap0000185
- Wang, Z., & Thompson, B. (2007). Is the Pearson r2 biased, and if so, what is the best correction formula? Journal of Experimental Education, 75(2), 109–125.
- West, S. M., Whittaker, M., & Crawford, K. (2019). Discriminating systems: Gender, race and power in AI. New York. Retrieved from https://ainowinstitute.org/discriminatingsystems.html
- Wikipedia. (2004). Triumph Acclaim. Retrieved from https://en.wikipedia.org/wiki/Triumph_Acclaim.
- Zajechowski, M. (2017). The lessons we can learn from bad data mistakes made throughout history. Retrieved from https://www.smartdatacollective.com/lessons-can-learn-bad-data-mistakes-made-throughout-history/.
- Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W. (2017) Men also like shopping: Reducing gender bias amplification using corpus-level constraints. Paper presented at the EMNLP, Copenhagen, Denmark. http://markyatskar.com//publications/bias.pdf