Abstract
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.
| Original language | English |
|---|---|
| Journal | Journal of Behavioral Decision Making |
| Number of pages | 20 |
| ISSN | 0894-3257 |
| DOIs | |
| Publication status | Published - 23 Oct 2019 |
Bibliographical note
Epub ahead of print. Published online 23 October 2019Keywords
- Algorithm aversion
- Augmented decision making
- Human-algorithm interaction
- Systematic review
Cite this
}
A Systematic Review of Algorithm Aversion in Augmented Decision Making. / Burton, Jason W.; Stein, Mari Klara; Jensen, Tina Blegind.
In: Journal of Behavioral Decision Making, 23.10.2019.Research output: Contribution to journal › Review › Research › peer-review
TY - JOUR
T1 - A Systematic Review of Algorithm Aversion in Augmented Decision Making
AU - Burton, Jason W.
AU - Stein, Mari Klara
AU - Jensen, Tina Blegind
N1 - Epub ahead of print. Published online 23 October 2019
PY - 2019/10/23
Y1 - 2019/10/23
N2 - 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.
AB - 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.
KW - Algorithm aversion
KW - Augmented decision making
KW - Human-algorithm interaction
KW - Systematic review
KW - Algorithm aversion
KW - Augmented decision making
KW - Human-algorithm interaction
KW - Systematic review
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U2 - 10.1002/bdm.2155
DO - 10.1002/bdm.2155
M3 - Review
AN - SCOPUS:85074588387
JO - Journal of Behavioral Decision Making
JF - Journal of Behavioral Decision Making
SN - 0894-3257
ER -