Artificial intelligence (AI) models for decision support have been developed for clinical settings such as radiology, but little work evaluates the potential impact of such systems. In this study, physicians received chest X-rays and diagnostic advice, some of which was inaccurate, and were asked to evaluate advice quality and make diagnoses. All advice was generated by human experts, but some was labeled as coming from an AI system. As a group, radiologists rated advice as lower quality when it appeared to come from an AI system; physicians with less task-expertise did not. Diagnostic accuracy was significantly worse when participants received inaccurate advice, regardless of the purported source. This work raises important considerations for how advice, AI and non-AI, should be deployed in clinical environments.

Research has shown that framing decisions as gains or losses distorts human judgment. Human judgment is also assumed to be influenced by the actual level of construal. Whether decisions are construed in a more detailed manner (low level construal) or in a more abstract manner (high level construal) can depend on perceived psychological distance. In the present studies, we examined the influence of framing and psychological distance on risk taking. In three studies with students (n = 65), physicians (n = 60), and hotel managers (n = 39), we found evidence that construal level influences risk seeking in gain situations, but not in loss situations. Furthermore, the framing effect could be replicated in psychologically close situations, and was eliminated (Studies 1 and 2) or reversed (Study 3) in psychologically distant situations. Our findings illuminate the interplay of framing and construal level, and points out their applicability in organizational decision making.

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Self‐driving vehicles will affect the future of transportation, but factors that underlie perception and acceptance of self‐driving cars are yet unclear. Research on feelings as information and the affect heuristic has suggested that feelings are an important source of information, especially in situations of complexity and uncertainty. In this study (N = 1,484), we investigated how feelings related to traditional driving affect risk perception, benefit perception, and trust related to self‐driving cars as well as people's acceptance of the technology. Due to limited experiences with and knowledge of self‐driving cars, we expected that feelings related to a similar experience, namely, driving regular cars, would influence judgments of self‐driving cars. Our results support this assumption. While positive feelings of enjoyment predicted higher benefit perception and trust, negative affect predicted higher risk and higher benefit perception of self‐driving cars. Feelings of control were inversely related to risk and benefit perception, which is in line with research on the affect heuristic. Furthermore, negative affect was an important source of information for judgments of use and acceptance. Interest in using a self‐driving car was also predicted by lower risk perception, higher benefit perception, and higher levels of trust in the technology. Although people's individual experiences with advanced vehicle technologies and knowledge were associated with perceptions and acceptance, many simply have never been exposed to the technology and know little about it. In the absence of this experience or knowledge, all that is left is the knowledge, experience, and feelings they have related to regular driving.

In a series of studies, we examined the influence of people's mind-set (construal level (CL): abstract versus concrete) on their risktaking behavior. We measured differences in CL (study 1, CL as trait) and manipulated CL (studies 1-5, CL as state) with different priming methods, which were unrelated to the dependent variable of risk-taking behavior (studies 1, 3, 4, and 5: Balloon Analog Risk Task; study 2: Angling Risk Task). In all studies, abstract CL resulted in greater risk-taking compared with concrete CL, which led to lower risk-taking. Risky and safe game strategies mediated the CL effect on risk-taking. A concrete mind-set increased the safe game strategy, whereas an abstract mind-set increased the risky game strategy. Furthermore, different potential mediators were explored (i.e., focus on payoffs and probabilities, prevention versus promotion focus, attention to pros versus cons, and mood). A concrete mind-set increased prevention strategies and a negative mood when compared with an abstract mind-set. In turn, an abstract mind-set increased attention to pros (of an action). Copyright © 2014 John Wiley & Sons, Ltd.Can differences in mind-set influence risk-taking behavior? Traditionally, risk-taking was assumed to be a stable personality trait in that people can be grouped as either risk-seeking versus avoiding (Eysenck & Eysenck, 1977;Hanoch, Johnson, & Wilke, 2006;Zuckerman & Kuhlman, 2000). This was then expanded to include other considerations (Johnson, Wilke, & Weber, 2004). Risk behavior is now more broadly described with foci on its multidimensional nature (e.g., Figner & Weber, 2011). Many research on prospect theory (Kahneman & Tversky, 1979) shows that gain versus loss frames change the way people represent events, which in turn influences their preferences. Today, commonly observed influences on risk behavior include subtraits such as sensation seeking (e.g., Zuckerman, 2007), self-monitoring (e.g., positive relationship between self-monitoring in the form of public performing and risk-taking behavior; Bell, Schoenrock, & O'Neal, 2000), constructs such as mood (e.g., De Vries, Holland, & Witteman, 2008;Yuen & Lee, 2003), or visceral influences (e.g., visceral cues that indicate proximity to desired objects can lead to decisions that are less sensitive to risk information; Ditto, Pizarro, Epstein, Jacobson, & MacDonald, 2006). These factors contribute to the common observation that one sometimes chooses to play it safe when faced with risky situations and other times not (Freeman & Muraven, 2010). Freeman and Muraven (2010) raised the question as to what generates people's inconsistency in risk-taking behavior.This question is addressed here using a novel approach, that is, construal level (CL) (Trope & Liberman, 2010). On the basis of CL theory (CLT), we assume that risk-taking may be broadly influenced not only by personality traits, such as sensation seeking; gain versus loss frames; and situational factors, such as affect, but also by people's cognitive mind-set (CL). CONSTRUAL LE...