Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence

89 Pages Posted: 21 Dec 2022 Last revised: 11 Mar 2024

See all articles by Maya Balakrishnan

Maya Balakrishnan

Harvard Business School

Kris Ferreira

Harvard Business School

Jordan Tong

Wisconsin School of Business

Date Written: February 29, 2024

Abstract

Even if algorithms make better predictions than humans on average, humans may sometimes have private information which an algorithm does not have access to that can improve performance. How can we help humans effectively use and adjust recommendations made by algorithms in such situations? When deciding whether and how to override an algorithm’s recommendations, we hypothesize that people are biased towards following a naive advice weighting (NAW) heuristic: they take a weighted average between their own prediction and the algorithm’s, with a constant weight across prediction instances, regardless of whether they have valuable private information. This leads to humans over-adhering to the algorithm’s predictions when their private information is valuable and under-adhering when it is not. In an online experiment where participants are tasked with making demand predictions for 20 products while having access to an algorithm’s predictions, we confirm this bias towards NAW and find that it leads to a 20-61% increase in prediction error. In a second experiment, we find that feature transparency – even when the underlying algorithm is a black box – helps users more effectively discriminate how to deviate from algorithms, resulting in a 25% reduction in prediction error. We make further improvements in a third experiment via an intervention designed to get users to move away from advice weighting heuristics altogether and instead use only their private information to inform deviations, leading to a 34% reduction in prediction error.

Keywords: human-algorithm interaction, forecasting, behavioral operations, cognitive bias, algorithm transparency, wisdom of crowds, advice taking

Suggested Citation

Balakrishnan, Maya and Ferreira, Kris and Tong, Jordan, Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence (February 29, 2024). Available at SSRN: https://ssrn.com/abstract=4298669 or http://dx.doi.org/10.2139/ssrn.4298669

Maya Balakrishnan (Contact Author)

Harvard Business School ( email )

Boston, MA 02163
United States

Kris Ferreira

Harvard Business School ( email )

Boston, MA 02163
United States
617-495-3316 (Phone)

HOME PAGE: http://www.hbs.edu/faculty/Pages/profile.aspx?facId=773347

Jordan Tong

Wisconsin School of Business ( email )

975 University Avenue
Madison, WI 53706
United States

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