When Algorithms Err: Differential Impact of Early vs. Late Errors on Users' Reliance on Algorithms

22 Pages Posted: 2 Nov 2020

See all articles by Antino Kim

Antino Kim

Indiana University - Kelley School of Business - Department of Operation & Decision Technologies

Mochen Yang

University of Minnesota - Twin Cities - Carlson School of Management

Jingjng Zhang

Indiana University - Kelley School of Business - Department of Operation & Decision Technologies

Date Written: July 2020

Abstract

Errors are a natural part of the development and use of predictive algorithms, but they could discourage people from relying on algorithms even when doing so could lead to better decisions. In this paper, we conduct two experiments to demonstrate that people's reliance on a predictive algorithm following a substantial error depends on when the error occurs and how the algorithm is used in decision making. We find that, when the prediction tasks are fully delegated to an algorithm, the impact of an error on reliance is different if the error occurs early versus late. While an early error results in substantial and persistent reliance reduction, a late error affects reliance only temporarily. However, when users have more control over how to use the algorithm's predictions, the risk associated with early errors decreases. Our work advances the understanding of algorithm aversion and informs the practical design of algorithmic decision-making systems.

Keywords: Algorithm, decision support, prediction, timing of error, reliance

Suggested Citation

Kim, Antino and Yang, Mochen and Zhang, Jingjng, When Algorithms Err: Differential Impact of Early vs. Late Errors on Users' Reliance on Algorithms (July 2020). Available at SSRN: https://ssrn.com/abstract=3691575 or http://dx.doi.org/10.2139/ssrn.3691575

Antino Kim

Indiana University - Kelley School of Business - Department of Operation & Decision Technologies ( email )

Business 670
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Mochen Yang (Contact Author)

University of Minnesota - Twin Cities - Carlson School of Management ( email )

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Minneapolis, MN 55455
United States

Jingjng Zhang

Indiana University - Kelley School of Business - Department of Operation & Decision Technologies ( email )

1309 E. Tenth Street
HH4143
Bloomington, IN 47401
United States

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