Abstract
A longstanding finding in the judgment and decision-making literature is that human decision performance can be improved with the help of a mechanical aid. Despite this observation and celebrated advances in computing technologies, recently presented evidence of algorithm aversion raises concerns about whether the potential of human-machine decision-making is undermined by a human tendency to discount algorithmic outputs. In this chapter, we examine the algorithm aversion phenomenon and what it means for judgment in predictive analytics. We contextualize algorithm aversion in the broader human vs. machine debate and the augmented decision-making literature before defining algorithm aversion, its implications, and its antecedents. Finally, we conclude with proposals to improve methods and metrics to help guide the development of human-machine decision-making.
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Notes
- 1.
- 2.
The other primary opposition came decades later in Gary Klein’s (1993, 1997, 2008) studies on naturalistic decision-making. These studies promote reliance on expert intuition by focusing on real world contexts marked by time pressure and high-stake consequences, rather than artificial experiments. While Kahneman acted as his contemporary adversary, they reconciled their positions by agreeing that the comparative performance of human versus machine judgment depends on the environment in which it takes place (Kahneman & Klein, 2009).
- 3.
Bayes’ theorem is a mathematical formula for calculating a conditional probability that a hypothesis is true given some evidence (for an in depth review see Joyce, 2003).
- 4.
- 5.
These five factors fall under the fives themes presented in Burton et al. (2020): expectations and expertise, decision autonomy, incentivization, cognitive compatibility, and divergent rationalities.
- 6.
We note that in experimental set ups using variants of the WOA measure (e.g., Logg et al., 2019; Önkal et al., 2009), decision accuracy is often artificially accounted for because the machine aid’s judgment (or advice) is guaranteed to be highly accurate by the experimenter. However, this is not necessarily guaranteed in the real world, where achieving a high WOA may be undesirable in some circumstances (i.e, complacency bias or automation bias, Baudel et al., 2021; Parasuraman & Manzey, 2010; Zerilli et al., 2019).
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Burton, J.W., Stein, MK., Jensen, T.B. (2023). Beyond Algorithm Aversion in Human-Machine Decision-Making. In: Seifert, M. (eds) Judgment in Predictive Analytics. International Series in Operations Research & Management Science, vol 343. Springer, Cham. https://doi.org/10.1007/978-3-031-30085-1_1
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DOI: https://doi.org/10.1007/978-3-031-30085-1_1
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