People prefer algorithmic to human judgment.
Even though computational algorithms often outperform human judgment, received wisdom suggests that people may be skeptical of relying on them (Dawes, 1979). Counter to this notion, results from six experiments show that lay people adhere more to advice when they think it comes from an algorithm than from a person. People showed this sort of algorithm appreciation when making numeric estimates about a visual stimulus and forecasts about the popularity of songs and romantic matches. Yet, researchers predicted the opposite result. Algorithm appreciation persisted when advice appeared jointly or separately. However, algorithm appreciation waned when: people chose between an algorithm’s estimate and their own (versus an external advisor’s) and they had expertise in forecasting. Paradoxically, experienced professionals, who make forecasts on a regular basis, relied less on algorithmic advice than lay people did, which hurt their accuracy. These results shed light on the important question of when people rely on algorithmic advice over advice from people and have implications for the use of “big data” and algorithmic advice it generates.
Theory of Machine
Research on what I call Theory of Machine is needed to keep up with the rapid pace of technological advancement that injects algorithms into many aspects of our lives. By theory of machine (a kind of theory of mind), I mean lay theories about how algorithmic judgment works. My program of work examines people’s perceptions of how algorithmic and human judgment differ, in terms of input, process, and output.
Media: Work covered in Harvard Business Review, Technology Section.
This paper is in collaboration with: Julia A. Minson & Don A. Moore
Thank you also to the Intelligence Advanced Research Projects Activity (IARPA), the UC Berkeley Haas Dissertation Fellowship, and the Behavioral Lab at the Haas School of Business for their generous financial support.