Better explaining the benefits why AI? Analyzing the impact of explaining the benefits of AI-supported selection on applicant responses

Despite the increasing popularity of AI-supported selection tools, knowledge about the actions that can be taken by organizations to increase AI acceptance is still in its infancy, even though multiple studies point out that applicants react negatively to the implementation of AI-supported selection tools. Therefore, this study investigates ways to alter applicant reactions to AI-supported selection. Using a scenario-based between-subject design with participants from the working population (N = 200), we varied the information provided by the organization about the reasons for using an AI-supported selection process (no additional information vs. written information vs. video information) in comparison to a human selection process. Results show that the use of AI without information and with written information decreased perceived fairness, personableness perception, and increased emotional creepiness. In turn, perceived fairness, personableness perceptions, and emotional creepiness mediated the association between an AI-supported selection process, organizational attractiveness, and the intention to further proceed with the selection process. Moreover, results did not differ for applicants who were provided video explanations of the benefits of AI-supported selection tools and those who participated in an actual human selection process. Important implications for research and practice are discussed.     

A Neural Network Model for Attribute-Based Decision Processes

We propose a neural model of multiattribute-decision processes, based on an attractor neural network with dynamic thresholds. The model may be viewed as a generalization of the elimination by aspects model, whereby simultaneous selection of several aspects is allowed. Depending on the amount of synaptic inhibition, various kinds of scanning strategies may be performed, leading in some cases to vacillations among the alternatives. The model predicts that decisions of a longer time duration exhibit a lower violation of the simple scalability law, as opposed to shorter decisions. Furthermore, the model is suggested as a general attribute-based decision module. Accordingly, various decision strategies are manifested depending on the module's parameters.