A systematic review of algorithm aversion in augmented decision making

By Jason Burton, Mari-Klara Stein, Tina Blegind Jensen. Journal of Behavioral Decision Making, 2019

637 citations recorded by the source corpus.

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.

Subfields: General Decision Sciences, Safety Research, Social Psychology

View Paper: https://www.semanticscholar.org/paper/20452754227ec9d48ee3c68f45b6eb64b6766a74

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Citations in the corpus

Forecasting: theory and practice (2020, background): “Studies examining individual’s trust in model versus expert forecasts show that individuals often distrust algorithms (Meehl, 2013; Burton et al., 2020) and place higher trust on human advice (Diab et al.”
Artificial intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry (2020, background): “Although first algorithms reach, and even surpass, human capacities in many narrow tasks, humans often show a general aversion to adopting algorithms (for a review, see Burton et al., 2019).”
Augmenting Medical Diagnosis Decisions? An Investigation into Physicians' Decision-Making Process with Artificial Intelligence (2021, background): “…currently focuses on how to develop more accurate and transparent AI-based systems to counter these challenges (Rai 2020), too little effort is spent on understanding the cognitive challenges of decision makers that hinder successful decision augmentation with AI advice (Burton et al. 2020).”
The Next Generation of Research on IS Use: A Theoretical Framework of Delegation to and from Agentic IS Artifacts (2021, background): “(Burton et al. 2019) are particularly important as well as the level of control retained or”
Algorithmic management in a work context (2021, background): “The extra effort needed to draw on algorithmic decisions also comes with heavy investments of time and energy, such as aligning human and algorithmic cognitive systems (Burton et al., 2020).”