Algorithm appreciation: People prefer algorithmic to human judgment

Logg, Jennifer M.; Minson, Julia A.; Moore, Don A.

doi:10.1016/j.obhdp.2018.12.005

Cited by 802 publications

(674 citation statements)

References 58 publications

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“…While robo-fund aversion (see Fig. 2 for an illustration of this aversion on pooled data) is consistent with the literature on algorithm aversion (Dietvorst et al, 2015), and aversion towards machines making moral decisions (Bigman & Gray, 2018), it should not be treated as an obvious finding, considering that researchers have shown algorithm appreciation under certain conditions (Logg et al, 2019), and were unable to show algorithm aversion in financial investment (Germann & Merkle, 2019). We should also add that while an internal meta-analysis (Goh et al, 2016) suggests a mean effect size of dweighted = -0.58 across all three studies, our use of the term "computer algorithm" can be considered a conservative measure.…”

Section: Discussionsupporting

confidence: 73%

Robo-investment aversion

Niszczota¹,

Kaszás²

2020

Preprint

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In three experiments (N = 2380), we show that people find it more permissible when investment decisions concerning controversial (“sin”) stocks are made by human fund managers rather than by computers (“robos”). In Study 1 (N = 466), participants rated the permissibility of a computer (algorithm) to autonomously exclude morally controversial stocks from investment portfolios as lower than if a human fund manager did the same; this finding was not different if participants were informed that such exclusions might be financially disadvantageous for them. In Study 2 (N = 1231), we show that this robo-fund aversion manifests itself both when considering investment in controversial and non-controversial industries. In Study 3 (N = 683), our findings show that robo-fund aversion is also present when algorithms are given the autonomy to increase investment in controversial stocks. Our findings suggest that algorithm aversion extends to the financial realm, supporting the existence of a barrier for the adoption of innovative financial technologies (FinTech), but one that might be overcome by hybrid (human-in-the-loop) solutions.

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Section: Discussionsupporting

confidence: 73%

Robo-investment aversion

Niszczota¹,

Kaszás²

2020

Preprint

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“…Although a theory of (algorithmic) mind (cf. theory of machine, Logg, Minson, & Moore, ) naturally applies to the idea of algorithmic literacy, its connections to other themes are also apparent: Does an accurate internal model of an algorithm's perceptions moderate the degree to which a human user feels a need for control, the degree to which a user requires extrinsic incentivization, the degree to which a user is capable of integrating an algorithm's decision process, or the degree to which a user is able to align with an algorithm's rational decision outcome?…”

Section: Discussionmentioning

confidence: 99%

A systematic review of algorithm aversion in augmented decision making

Burton

Stein

Jensen

2019

Behavioral Decision Making

368

222

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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.

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“…In some recent studies, Logg [2,3] has shown that people trust more a machine than other people when they need to make a decision in an objective context (e.g., they are looking for information). In other studies, in a subjective context (e.g., looking for book recommendations, looking for joke recommendations), people tend to rely more on other human beings [4,5].…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…Given the theoretical framework described above, and in particular following the results on decisions' outcomes of Logg [2,3], we expected that a suggestion provided by an intelligent machine (in a technical and relatively complicated decision task) decreases the possibility for the user to blame the advisor and therefore should produce a lower rating on counterfactual emotions and responsibility.…”

Section: Hypothesesmentioning

confidence: 99%

Following Wrong Suggestions: Self-blame in Human and Computer Scenarios

Beretta

Zancanaro

Lepri

2019

Human-Computer Interaction – INTERACT 2019

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This paper investigates the specific experience of following a suggestion by an intelligent machine that has a wrong outcome and the emotions people feel. By adopting a typical task employed in studies on decision-making, we presented participants with two scenarios in which they follow a suggestion and have a wrong outcome by either an expert human being or an intelligent machine. We found a significant decrease in the perceived responsibility on the wrong choice when the machine offers the suggestion. At present, few studies have investigated the negative emotions that could arise from a bad outcome after following the suggestion given by an intelligent system, and how to cope with the potential distrust that could affect the long-term use of the system and the cooperation. This preliminary research has implications in the study of cooperation and decision making with intelligent machines. Further research may address how to offer the suggestion in order to better cope with user's self-blame.

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Algorithm appreciation: People prefer algorithmic to human judgment

Cited by 802 publications

References 58 publications

Robo-investment aversion

Robo-investment aversion

A systematic review of algorithm aversion in augmented decision making

Following Wrong Suggestions: Self-blame in Human and Computer Scenarios

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