The Psychology of Big Data: Developing a 'Theory of Machine' to Examine Perceptions of Algorithms
Logg, J. M. (2022). The psychology of Big Data: Developing a “theory of machine” to examine perceptions of algorithms. In S. C. Matz (Ed.), The psychology of technology: Social science research in the age of Big Data (pp. 349–378). American Psychological Association. https://doi.org/10.1037/0000290-
37 Pages Posted: 26 Jul 2021 Last revised: 23 Jun 2023
Date Written: June 15, 2021
Historically, people have informed their decisions with advice from other people. However, the rise of “big data” has increased both the availability and utility of a new source of advice: algorithms. These scripts for mathematical calculations cull through massive amounts of data and produce insights that can improve decision making. Many organizations are already trying to capture this potential, using algorithms to hire promising applicants (e.g., Amazon), predict performance of current employees (e.g., Navy Seals, National Football League teams, and Premier League soccer teams), and identify individuals who are likely to leave in order to improve retention (e.g., Johnson & Johnson).
While organizations are swimming in data and investing in algorithms, many are trying to understand how to maximize the benefits of algorithmic advice. And while companies focus on producing more analytical insights, it is not clear how well those insights are utilized. What happens when algorithmic advice lands in the hands of managers and other decision makers; when do they listen to it and when do they disregard it?
One article aptly labeled the gap between producing and utilizing insights from algorithms as the Last Mile Problem. The “last mile” concept is commonly used in supply chain management to describe how goods are transported from a centralized hub to the final end user. In data analytics, the last mile problem describes the issue of producing analytical insights but 1) failing to communicate them at all, leading to wasted information, or 2) failing to communicate them clearly, leading to misapplied information. This problem is similar to a soccer player with great footwork who fails to convert plays into goals or assists. Failing to communicate analytical results clearly hinders decisionmakers from acting on them. In an article describing predictive analytics, Schrage eloquently states, “Effectively communicating and sharing analytic insights is as important as finding them.”
Data analytics needs psychology. Organizations cannot realize the full potential of algorithms until they address the last mile problem and consider how people respond to algorithmic advice. Algorithms have the potential to greatly improve human judgment and decision making, as they generally outperform the accuracy of experts when the two are directly compared. But people can only leverage the accuracy of algorithms if they are willing to listen. Should they ignore algorithmic advice, the resources invested into data analytics, both within academia and industry, will go to waste. While the field of data analytics (the systematic computation of data, most commonly using algorithms) continues to evolve at a rapid rate, most overlook the important connection between producing and utilizing insights.
Keywords: Big Data, Algorithms, Psychology, Decision Making, Theory of Machine
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