Abstract
Discussions about the potential for artificial intelligence (AI) in modern society have ranged across virtually every aspect of human endeavors. At least at present, general-purpose artificial intelligence remains quite distant. The best prospects lie in developing evidence-based algorithms to aid decisions especially in those situations where human judgment is now dominant. Peter Cappelli, Prasand Tambe, and Valery Yakubovich consider the possibilities for data science-based algorithms in one area of organizational and business life, and that is human resource decisions. The need for better and more objective decisions here is evident. It is also clear that the realities and context of human resources is often lost on data scientists racing to introduce tools developed in other contexts. They outline the opportunities and limitations for using these algorithms in human resources and the management of employees.
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Notes
- 1.
See, e.g., https://www.jobtestprep.co.uk/lloydsbank.
References
Barends, Eric, and Denise M. Rousseau. 2018. Evidence-Based Management: How to Use Evidence to Make Better Organizational Decisions. London: Kogan Page.
Bernstein, Ethan, Saravanan Kesavan, and Bradley Staats. 2014. How to Manage Scheduling Software Fairly. Harvard Business Review, December. https://hbr.org/2014/09/how-to-manage-scheduling-software-fairly.
Bloomberg, J. 2018. Don’t Trust Artificial Intelligence? Time to Open the AI Black Box. Forbes, November 27. Last accessed at https://www.forbes.com/sites/jasonbloomberg/2018/09/16/dont-trust-artificial-intelligence-time-to-open-the-ai-black-box/#577a14153b4a.
Bock, Laslo. 2015. Work Rules! Insights from Inside Google That Will Transform How You Live and Lead. London: Hachette Book Group.
Cappelli, Peter. 2017. There’s No Such Thing as Big Data in HR. Harvard Business Review, June.
Cappelli, Peter, and Anna Tavis. 2017. The Performance Management Revolution. Harvard Business Review, November.
Denrell, Jerker, Christina Fang, and Chengwei Liu. 2015. Change Explanations in Management Science. Organization Science 26 (3): 923–940.
Cowgill, Bo. 2017. The Labor Market Effects of Hiring Through Machine Learning. Working Paper.
Cowgill, Bo. 2018. Bias and Productivity in Humans and Algorithms: Theory and Evidence from Résumé Screening. Working Paper.
Dietvorst, Berkeley, Joseph P. Simmons, and Cade Massey. 2014. Algorithm Aversion: People Erroneously Avoid Algorithms After Seeing Them Err. Journal of Experimental Psychology: General 144 (1): 114.
Dwork, Cynthia, and Aaron Roth. 2014. The Algorithmic Foundations of Differential Privacy. Foundations and Trends in Theoretical Computer Science 9 (3–4): 211–407.
Fukolova, Julia. 2018. Frames Under the Numbers. Harvard Business Review, Russian edition. https://hbr-russia.ru/management/upravlenie-personalom/776169.
Hoffman, Mitchell, Lisa B. Kahn, and Danielle Li. 2015. Discretion in Hiring. NBER Working Paper 21709. http://www.nber.org/papers/w21709.
IBM. 2018. Unplug from the Past. 19th Global C-Suite Study. IBM Institute for Business Value.
Junqué de Fortuny, E., D. Martens, and F. Provost. 2013. Predictive Modeling with Big Data: Is Bigger Really Better? Big Data 1 (4): 215–226.
Lee, M.K., D. Kusbit, E. Metsky, and L. Dabbish. 2015. Working with Machines: The Impact of Algorithmic, Data-Driven Management on Human Workers. In Proceedings of the 33rd Annual ACM SIGCHI Conference: 1603–1612, ed. B. Begole, J. Kim, K. Inkpen, and W. Wood, ACM Press, New York, NY.
Lind, E. Allan, and Kees Van den Bos. 2002. When Fairness Works: Toward a General Theory of Uncertainty Management. Research in Organizational Behavior 24: 181–223.
Liu, Chengwei, and Jerker Denrell. 2018. Performance Persistence Through the Lens of Chance Models: When Strong Effects of Regression to the Mean Lead to Non-Monotonic Performance Associations. Working paper.
Loftus, Joshua R., Chris Russel, Matt J. Kusner, and Ricardo Silva. 2018. Causal Reasoning for Algorithmic Fareness. arXiv:1805.05859.
Malinsky, Daniel, and David Danks. 2017. Causal Discovery Algorithms: A Practical Guide. Philosophy Compass. https://doi.org/10.1111/phc3.12470.
March, James, and Herbert Simon. 1993. Organizations. Oxford: Blackwell.
Meyer, David. 2018. Amazon Reportedly Killed an AI Recruitment System Because It Couldn’t Stop the Tool from Discriminating Against Women. Fortune, October 10. http://fortune.com/2018/10/10/amazon-ai-recruitment-bias-women-sexist.
Monthly Labor Review. 2017. Estimating the U.S. Labor Share. Bureau of Labor Statistics, February. https://www.bls.gov/opub/mlr/2017/article/estimating-the-us-share.htm.
Netessine, Serguei, and Valery Yakubovich. 2012. The Darwinian Workplace. Harvard Business Review 90 (5): 25–28.
Pearl, Judea. 2018. The Book of Why: The New Science of Cause and Effect. New York: Basic Books.
Pfeffer, Jeffrey, and Robert I. Sutton. 2006. Hard Facts, Dangerous Half-Truths and Total Nonsense: Profiting from Evidence-Based Management. Boston: Harvard Business Review Press.
Schneider, Benjamin. 1987. The People Make the Place. Personnel Psychology 40 (3): 437–453.
Shrout, P.E., and J.L. Rodgers. 2018. Psychology, Science and Knowledge Construction: Broadening Perspectives from the Replication Crisis. Annual Review of Psychology 69: 487–510.
Spellman, B. 2015. A Short (Personal) Future History of Revolution 2.0. Perspectives on Psychological Science 10: 886–899.
Spielkamp, Michael. 2017. Inspecting Algorithms for Bias. MIT Technology Review, June 12. https://www.technologyreview.com/s/607955/inspecting-algorithms-for-bias/.
Stone, Peter. 2011. The Luck of the Draw: The Role of Lotteries in Decision Making. Oxford: Oxford University Press.
Tucker, Catherine. 2017. Privacy, Algorithms, and Artificial Intelligence. In The Economics of Artificial Intelligence: An Agenda, ed. Ajay K. Agrawal, Joshua Gans, and Avi Goldfarb, 423–437. Chicago: University of Chicago Press. http://www.nber.org/chapters/c14011.
Walsh, David J. 2013. Employment Law for Human Resource Practice. Mason, OH: South-Western CENGAGE Learning.
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Cappelli, P., Tambe, P., Yakubovich, V. (2020). Can Data Science Change Human Resources?. In: Canals, J., Heukamp, F. (eds) The Future of Management in an AI World. IESE Business Collection. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-20680-2_5
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