Empirically Understanding the Impact of Item Constraints on Designer Ideation

Chong, Leah; Goucher-Lambert, Kosa; Kotovsky, Kenneth; Cagan, Jonathan

doi:10.1007/978-3-030-90625-2_1

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…In contrast, Cheng et al (2019) found no discernible difference in trust between explanatory vs. non-explanatory conditions. Chong et al (2022) highlighted that trust levels are contingent on the AI system's perceived expertise. Participants were guided by an .…”

Section: Accurate Information Strategiesmentioning

confidence: 99%

Humans in XAI: increased reliance in decision-making under uncertainty by using explanation strategies

Lammert,

Richter,

Schütze

et al. 2024

Front. Behav. Econ.

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IntroductionAlthough decision support systems (DSS) that rely on artificial intelligence (AI) increasingly provide explanations to computer and data scientists about opaque features of the decision process, especially when it involves uncertainty, there is still only limited attention to making the process transparent to end users.MethodsThis paper compares four distinct explanation strategies employed by a DSS, represented by the social agent Floka, designed to assist end users in making decisions under uncertainty. Using an economic experiment with 742 participants who make lottery choices according to the Holt and Laury paradigm, we contrast two explanation strategies offering accurate information (transparent vs. guided) with two strategies prioritizing human-centered explanations (emotional vs. authoritarian) and a baseline (no explanation).Results and discussionOur findings indicate that a guided explanation strategy results in higher user reliance than a transparent strategy. Furthermore, our results suggest that user reliance is contingent on the chosen explanation strategy, and, in some instances, the absence of an explanation can also lead to increased user reliance.

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Section: Accurate Information Strategiesmentioning

confidence: 99%