For a discussion of human-computer symbiosis, see: Guszcza, Lewis, and Evans-Greenwood, Cognitive collaboration. A recent exploration of human-computer extended intelligence is Thomas Malone’s book Superminds. For a summary, see: Jim Guszcza and Jeff Schwartz, “Superminds: How humans and machines can work together,” Deloitte Review 24, January 28, 2019.

Jim Guszcza, Harvey Lewis, and Peter Evans-Greenwood, Cognitive collaboration: Why humans and computers think better together, Deloitte Insights, January 23, 2017.

The field of artificial intelligence is commonly agreed upon to originate at a 1956 conference held at Dartmouth University. The conference was convened by John McCarthy, who coined the term “artificial intelligence” and defined it as the science of creating machines “with the ability to achieve goals in the world.” Other participants included Marvin Minsky, Alan Newell, Claude Shannon, and Herbert Simon. Their goal was to create artificial general intelligence in the sense of simulating “every aspect of learning or any other feature of intelligence.” In contrast, the AI applications discussed here are all forms of narrow artificial intelligence: the ability to achieve specific goals commonly associated with human intelligence. For example, an AI capable of diagnosing a patient will not be capable of making a product recommendation. For further discussion and references, see: Jim Guszcza, “Smarter together: Why artificial intelligence needs human-centered design,” Deloitte Review 22, January 22, 2018.

For an extended discussion of this theme in the context of behavioral data, see: Jim Guszcza, David Schweidel, and Shantanu Dutta, “The personalized and the personal: Socially responsible innovation through big data,” Deloitte Review 14, January 18, 2014; for a discussion of human-centered AI, see: Fei-Fei Li, “How to make AI that’s good for people,” New York Times, March 7, 2018; for further perspectives on human-centered AI, see: Jim Guszcza, “Smarter together: Why artificial intelligence needs human-centered design,” Deloitte Review 22, January 22, 2018.

Marianne Bertrand and Sendhil Mullainathan, “Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination,” National Bureau of Economic Research, accessed November 21, 2019; further discussion is provided in: Jim Guszcza, Josh Bersin, and Jeff Schwartz, “HR for humans: How behavioral economics can reinvent HR,” Deloitte Review 18, January 25, 2016.

See: Richard H. Thaler and Cass R. Sunstein, “Who’s on first,” University of Chicago Law School, September 1, 2003; see also: Jim Guszcza, “The importance of misbehaving: A conversation with Richard Thaler,” Deloitte Review 18, January 25, 2016. The ability of even simple algorithms to outperform unaided expert judgment gave rise to the decades-long “Actuarial versus clinical judgment” initiative by Daniel Kahneman’s predecessor Paul Meehl. For a discussion of this phenomenon in the context of AI, see: Guszcza, Lewis, and Evans-Greenwood, “Cognitive collaboration.”

For an extended discussion, see: Jim Guszcza, “The last-mile problem: How data science and behavioral science can work together,” Deloitte Review 16, January 27, 2015.