A Systematic Review of Algorithm Aversion in Augmented Decision Making

Jason W. Burton*, Mari Klara Stein, Tina Blegind Jensen

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Publikation: Bidrag til tidsskriftReviewForskningpeer review

Abstrakt

Despite abundant literature theorizing societal implications of algorithmic decision making, relatively little is known about the conditions that lead to the acceptance or rejection of algorithmically generated insights by individual users of decision aids. More specifically, recent findings of algorithm aversion—the reluctance of human forecasters to use superior but imperfect algorithms—raise questions about whether joint human-algorithm decision making is feasible in practice. In this paper, we systematically review the topic of algorithm aversion as it appears in 61 peer-reviewed articles between 1950 and 2018 and follow its conceptual trail across disciplines. We categorize and report on the proposed causes and solutions of algorithm aversion in five themes: expectations and expertise, decision autonomy, incentivization, cognitive compatibility, and divergent rationalities. Although each of the presented themes addresses distinct features of an algorithmic decision aid, human users of the decision aid, and/or the decision making environment, apparent interdependencies are highlighted. We conclude that resolving algorithm aversion requires an updated research program with an emphasis on theory integration. We provide a number of empirical questions that can be immediately carried forth by the behavioral decision making community.
OriginalsprogEngelsk
TidsskriftJournal of Behavioral Decision Making
Vol/bind33
Udgave nummer2
Sider (fra-til)220-239
Antal sider20
ISSN0894-3257
DOI
StatusUdgivet - apr. 2020

Bibliografisk note

Published online 23 October 2019

Emneord

  • Algorithm aversion
  • Augmented decision making
  • Human-algorithm interaction
  • Systematic review

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