What People Want From Their Professionals: Attitudes Toward Decision‐making Strategies

Abstract

Attitudes toward four types of decision‐making strategies—clinical/fully rational, clinical/heuristic, actuarial/fully rational, and actuarial/heuristic—were examined across three studies. In Study 1, undergraduate students were split randomly between legal and medical decision‐making scenarios and asked to rate each strategy in terms of the following: (i) preference; (ii) accuracy; (iii) fairness; (iv) ethicalness; and (v) its perceived similarity to the strategies used by actual legal and medical professionals to make decisions. Studies 2 and 3 extended Study 1 by using a more relevant scenario and a community sample, respectively. Across the three studies, the clinical/fully rational strategy tended to be rated the highest across all attitudinal judgments, whereas the actuarial/heuristic strategy tended to receive the lowest ratings. Considering the two strategy‐differentiating factors separately, clinically based strategies tended to be rated higher than actuarially based strategies, and fully rational strategies were always rated higher than heuristic‐based strategies. The potential implications of the results for professionals' and those affected by their decisions are discussed. Copyright © 2011 John Wiley & Sons, Ltd.

A systematic review of algorithm aversion in augmented decision making

“…More recently, researchers have moved beyond the concept of cognitive style in favor of identifying specific heuristics and biases in human cognition that prevent decision makers from utilizing decision aids effectively. That is, although much attention is given to the opaque, black‐boxed nature of algorithms (Christin, ; Dietvorst et al, ; Eastwood et al, ), research suggests that human decision making operates through a black box of its own: intuition. For instance, decision aiding naturally expects a decision maker to adapt his intuition and/or deliberate analyses, but to do so, he would have to understand descriptively the mental processes underlying his unaided intuitive choice well enough to prescribe how to practically transform that intuition into the ideal judgement (Brown, , p. 217).…”

“…Existing research points out that organizational and social structures favor the expert intuiter over a cold algorithmic decision maker and incentivize accordingly (e.g., Alexander et al, 2018;Brown, 2015;Eastwood, Snook, & Luther, 2012;Highhouse, 2008b;Klimoski & Jones, 2008;Kuncel, 2008;Önkal et al, 2009). Brown (2015) and Hafenbrädl, Waeger, Marewski, and Gigerenzer (2016) argue that augmented decision making requires extra motivation because it involves combining multiple judgements rather than the acceptance of a single calculation.…”

“…There are often various stakeholders that each hold expectations and ideas of what constitutes a good decision, which may not necessarily include probabilistic accuracy. Because of this, decision makers are motivated to conform to social norms, be it a professional maintaining an aura of omniscience in front of clients/ patients (Arkes, Shaffer, & Medow, 2007;Eastwood et al, 2012) or an employee seeking the support of management (Sanders & Courtney, 1985). In fact, information about others' algorithm utilization (i.e., social incentivization) is shown to have a greater influence than information about the algorithm itself (i.e., algorithmic literacy) on decision makers' engagement and performance with algorithmic aids (Alexander et al, 2018).…”

Threat of Technological Unemployment: Are Hiring Managers Discounted for Using Standardized Employee Selection Practices?

“…Organizational researchers who are interested in promoting the use of best-practices are encouraged to examine the cultural (e.g., Boatman & Erker, 2012), organizational (e.g., Wilk & Cappellik, 2003), and personal (e.g., Nolan & Highhouse, 2014) attributes that influence decision making for employee selection. Likewise, additional insight may be provided by exploring issues related to judgment and decision making in other fields-especially those like medicine (e.g., Giluk & Rynes, 2012), auditing (e.g., Lowe, et al, 2002), and law (e.g., Eastwood, Snook, & Luther, 2012)-that are also experiencing difficulty convincing professionals to use the standardized practices that have been developed to aid decision making. Once the factors underlying practitioners' resistance to standardized hiring practices have been identified, this information can then be used to help organizational research design and implement employee selection systems that are more attractive to practitioners yet retain the predictive validity and legal defensibility of traditional standardized approaches (Kuncel, 2008).…”

Other-serving bias in advice-taking: When advisors receive more credit than blame

“…Although the use of these tools or even a complete reliance on these procedures replacing the human element have been shown to outperform clinical (i.e., non-mechanical) judgment, advisees are reluctant to take advice from these sources and view professionals who use mechanical methods as less competent (Eastwood, Snook, & Luther, 2012;Palmeira & Spassova, 2015). It would be interesting to examine whether the use of mechanical judgments affect perceptions of responsibility.…”