Repelled at first sight? Expectations and intentions of job-seekers reading about AI selection in job advertisements

Wesche, Jenny S.; Sonderegger, Andreas

doi:10.1016/j.chb.2021.106931

Cited by 31 publications

(21 citation statements)

References 54 publications

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Section: Resultsmentioning

confidence: 99%

“…An interview was conducted in the last phase of the research to evaluate how prone the PF is to implement and use this technology. Considering another study (Wesche and Sonderegger, 2021) and the opinion of the experts, the advantages and disadvantages of implementing AI in the evaluation and selection phases of the recruitment process in PJ can be inferred. The combination of AI and human decision-making indicates comments expressing positive attitudes towards a selection process in which an AI takes decisions in the screening stage.…”

Section: To-be Diagrammentioning

confidence: 99%

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AI and public contests: a model to improve the evaluation and selection of public contest candidates in the Police Force

Goncalves

Anastasiadou

Santos

2022

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Purpose The number of candidates applying to public contests (PC) is increasing compared to the number of human resources employees required for selecting them for the Police Force (PF). This work intends to perceive how those public institutions can evaluate and select their candidates efficiently during the different phases of the recruitment process. To achieve this purpose, artificial intelligence (AI) was studied. This paper aims to focus on analysing the AI technologies most used and appropriate to the PF as a complementary recruitment strategy of the National Criminal Investigation police agency of Portugal – Polícia Judiciária. Design/methodology/approach Using design science research as a methodological approach, the authors suggest a theoretical framework in pair with the segmentation of the candidates and comprehend the most important facts facing public institutions regarding the usage of AI technologies to make decisions about evaluating and selecting candidates. Following the preferred reporting items for systematic reviews and meta-analyses methodology guidelines, a systematic literature review and meta-analyses method was adopted to identify how the usage and exploitation of transparent AI positively impact the recruitment process of a public institution, resulting in an analysis of 34 papers between 2017 and 2021. Findings Results suggest that the conceptual pairing of evaluation and selection problems of candidates who apply to PC with applicable AI technology such as K-means, hierarchical clustering, artificial neural network and convolutional neural network algorithms can support the recruitment process and could help reduce the workload in the entire process while maintaining the standard of responsibility. The combination of AI and human decision-making is a fair, objective and unbiased process emphasising a decision-making process free of nepotism and favouritism when carefully developed. Innovative and modern as a category, group the statements that emphasise the innovative and contemporary nature of the process. Research limitations/implications There are two main limitations in this study that should be considered. Firstly, the difficulty regarding the timetable, privacy and legal issues associated with public institutions. Secondly, a small group of experts served as the validation group for the new framework. Individual semi-structured interviews were conducted to alleviate this constraint. They provide additional insights into an interviewee’s opinions and beliefs. Social implications Ensure that the system is fair, transparent and facilitates their application process. Originality/value The main contribution is the AI-based theoretical framework, applicable within the analysis of literature papers, focusing on the problem of how the institutions can gain insights about their candidates while profiling them, how to obtain more accurate information from the interview phase and how to reach a more rigorous assessment of their emotional intelligence providing a better alignment of moral values. This work aims to improve the decision-making process of a PF institution recruiter by turning it into a more automated and evidence-based decision when recruiting an adequate candidate for the job vacancy.

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Section: Resultsmentioning

confidence: 99%

Section: To-be Diagrammentioning

confidence: 99%

AI and public contests: a model to improve the evaluation and selection of public contest candidates in the Police Force

Goncalves

Anastasiadou

Santos

2022

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“…Mentioned previously, the literature suggests that AI in an assessment process reduces applicants’ assessment quality perceptions (e.g., Blacksmith et al, 2016 ; Wesche and Sonderegger, 2021 ), in spite of potential benefits such as reduced bias ( Quillian et al, 2017 ), or the potential to tap previously unknown sources of information (e.g., personality; Tengai AB, 2019 ). Woods et al (2020) provide an overview of potential concerns that limit assessment quality perceptions ranging from concerns on the validity of the capabilities of the AI, potential technology-related biases, the applicant’s insufficient digital familiarity, to privacy concerns.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…The aim of this study was to provide insights into assessment quality perceptions in various AI assessment situations, all in a personnel selection scenario. While using AI in personnel selection has been on the rise and has become increasingly popular among recruiters in the field of HR ( Upadhyay and Khandelwal, 2018 ; Woods et al, 2020 ), empirical evidence points to the fact that applicants may not view AI in personnel selection favorably (e.g., Blacksmith et al, 2016 ; Wesche and Sonderegger, 2021 ). For example, applicants may feel that if personal exchange reduces, they have fewer opportunities to perform ( Langer et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Dear Computer on My Desk, Which Candidate Fits Best? An Assessment of Candidates’ Perception of Assessment Quality When Using AI in Personnel Selection

Schick

Fischer

2021

Front. Psychol.

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Recently, with the increase in technological capabilities and the need to reduce bias in candidate selection processes, artificial intelligence (AI)-based selection procedures have been on the rise. However, the literature indicates that candidate reactions to a selection process need to be considered by organizations that compete for employees. In this study, we investigate reactions to AI-based selection procedures in a three-dimensional vignette study among young adults in Germany. By investigating the effects of the dimensions of AI complexity, intangibility, and reliability on the perceived quality of assessment of potential candidates, we found that AI complexity and intangibility impact the perceived quality of assessment negatively when the candidates’ knowledge, strengths, and weaknesses should be assessed. We also found interactive relationships of all three dimensions for the assessment of motivation. In sum, results indicate that candidates are skeptical toward the assessment quality of AI-intense selection processes, especially if these assess complex assessment criteria such as personality or a job performance forecast. Hence, organizations need to be careful when implementing AI-based selection procedures. HR implications are made on the basis of these results to cope with negative candidate perceptions.

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“…Previous research showed that applicant reactions to AI‐supported selection tools are predominantly negative in terms of justice perceptions (e.g., Acikgoz et al, 2020; Köchling et al, 2022; Langer & Landers, 2021; Langer et al, 2020; Newman et al, 2020; Wesche & Sonderegger, 2021). While taking advantage of AI‐supported selection tools and not discouraging applicants and keeping them in the selection process at the same time, it is paramount to examine the possible actions that organizations can take to improve applicant reactions in personnel selection.…”

Section: Introductionmentioning

confidence: 99%

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

Köchling

Wehner

2022

Int J Selection Assessment

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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.

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Repelled at first sight? Expectations and intentions of job-seekers reading about AI selection in job advertisements

Cited by 31 publications

References 54 publications

AI and public contests: a model to improve the evaluation and selection of public contest candidates in the Police Force

AI and public contests: a model to improve the evaluation and selection of public contest candidates in the Police Force

Dear Computer on My Desk, Which Candidate Fits Best? An Assessment of Candidates’ Perception of Assessment Quality When Using AI in Personnel Selection

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

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