Understanding algorithm aversion: When is advice from automation discounted?

• Author: Andrew Prahl,Lyn Van Swol
• Published date: 01 September 2017
• Date: 01 September 2017
• DOI: http://doi.org/10.1002/for.2464

RESEARCH ARTICLE

Understanding algorithm aversion: When is advice from

automation discounted?

Andrew Prahl | Lyn Van Swol

Department of Communication Arts,

University of Wisconsin–Madison,

Wisconsin, USA

Correspondence

Andrew Prahl, Department of

Communication Arts, University of

Wisconsin–Madison, Madison, WI 53706,

USA.

Email: aprahl@wisc.edu

Abstract

Forecasting advice from human advisors is often utilized more than advice from

automation. There is little understanding of why “algorithm aversion”occurs, or

specific conditions that may exaggerate it. This paper first reviews literature from

two fields—interpersonal advice and human–automation trust—that can inform

our understanding of the underlying causes of the phenomenon. Then, an experi-

ment is conducted to search for these underlying causes. We do not replicate the

finding that human advice is generally utilized more than automated advice. How-

ever, after receiving bad advice, utilization of automated advice decreased signifi-

cantly more than advice from humans. We also find that decision makers describe

themselves as having much more in common with human than automated advisors

despite there being no interpersonal relationship in our study. Results are discussed

in relation to other findings from the forecasting and human–automation trust fields

and provide a new perspective on what causes and exaggerates algorithm aversion.

KEYWORDS

advice, algorithmaversion, automation, computers, trust

1|INTRODUCTION

Computers, robots, algorithms, and other forms of automa-

tion are quickly becoming a fundamental part of many deci-

sion‐making processes in both personal and professional

contexts. From forecasting product sales (Fildes, Goodwin,

Lawrence, & Nikolopoulos, 2009) to informing medical and

management decisions (Esmaeilzadeh, Sambasivan, Kumar,

& Nezakati, 2015; Inthorn, Tabacchi, & Seising, 2015; Prahl,

Dexter, Braun, & Van Swol, 2013), people frequently seek

and receive advice from nonhuman (automation) sources

when facing important decisions. Yet, despite seeking advice

from automation, decision makers frequently discount advice

obtained from it, especially when compared to advice from a

human advisor (Önkal, Goodwin, Thomson, Gönül, &

Pollock, 2009).

The irrational discounting of automation advice has long

been known and a source of the spirited “clinical versus actu-

arial”debate in clinical psychology research (Dawes, 1979;

Meehl, 1954). Recently, this effect has been noted in forecast-

ing research (Önkal et al., 2009) and has been called algo-

rithm aversion (Dietvorst, Simmons, & Massey, 2015). A

developing area of research is trying to identify interventions

that increase trust in automation advice, such as providing

confidence intervals or allowing human judges to slightly

modify automation forecasts (Dietvorst, Simmons, &

Massey, 2016; Goodwin, Gönül, & Önkal, 2013). This

research is important, but more research is needed on the

underlying psychological processes that affect the

discounting of automation advice, especially in comparison

to human advice. This paper examines trust as a factor that

may underlie differences in use of advice. First, we summa-

rize literature in two related fields that can inform forecasting

research about algorithm aversion: interpersonal advice and

human–automation trust.

In addition to providing a new perspective on automated

advice, both the advice field and human factors provide the-

oretical frameworks that we use to generate hypotheses and

Received: 20 May 2016 Revised: 1 December 2016 Accepted: 31 January 2017

DOI: 10.1002/for.2464

Journal of Forecasting. 2017;36:691–702. Copyright © 2017 John Wiley & Sons, Ltd.wileyonlinelibrary.com/journal/for 691