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

Köchling, Alina; Wehner, Marius Claus

doi:10.1111/ijsa.12412

Cited by 10 publications

(5 citation statements)

References 85 publications

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“…Recent research showed that algorithm-driven hiring processes are perceived as less fair compared to human-only decisions by candidates ( Lavanchy et al, 2023 ) and that people feel less capable of influencing the outcome of an algorithm compared to human judgment ( Li et al, 2021 ; Hilliard et al, 2022 ). Interestingly, fairness mediates the association between an algorithm-based selection process and organizational attractiveness and the intention to further proceed with the selection process ( Köchling and Wehner, 2022 ). Consequently, it is in the best interest of employers to utilize personality-based algorithms, due to their increased fairness, to improve their attractiveness among potential candidates.…”

Section: Discussionmentioning

confidence: 99%

Gender equity in hiring: examining the effectiveness of a personality-based algorithm

Kubiak,

Efremova,

Baron

et al. 2023

Front. Psychol.

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IntroductionGender biases in hiring decisions remain an issue in the workplace. Also, current gender balancing techniques are scientifically poorly supported and lead to undesirable results, sometimes even contributing to activating stereotypes. While hiring algorithms could bring a solution, they are still often regarded as tools amplifying human prejudices. In this sense, talent specialists tend to prefer recommendations from experts, while candidates question the fairness of such tools, in particular, due to a lack of information and control over the standardized assessment. However, there is evidence that building algorithms based on data that is gender-blind, like personality - which has been shown to be mostly similar between genders, and is also predictive of performance, could help in reducing gender biases in hiring. The goal of this study was, therefore, to test the adverse impact of a personality-based algorithm across a large array of occupations.MethodThe study analyzed 208 predictive models designed for 18 employers. These models were tested on a global sample of 273,293 potential candidates for each respective role.ResultsMean weighted impact ratios of 0.91 (Female-Male) and 0.90 (Male-Female) were observed. We found similar results when analyzing impact ratios for 21 different job categories.DiscussionOur results suggest that personality-based algorithms could help organizations screen candidates in the early stages of the selection process while mitigating the risks of gender discrimination.

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Section: Discussionmentioning

confidence: 99%

Gender equity in hiring: examining the effectiveness of a personality-based algorithm

Kubiak,

Efremova,

Baron

et al. 2023

Front. Psychol.

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“…Otting and Maier, 2018;Tambe et al, 2019). This effect manifests in employees' feelings of inequality, perceived unfairness, skepticism toward organizational intentions, doubts about the appropriateness of AI-enabled HRM, reluctance to accept AI recommendations and heightened anxiety (K€ ochling and Wehner, 2023;Robert et al, 2020).…”

Section: Risk For Ai Algorithmic Opacitymentioning

confidence: 99%

The dark side of AI-enabled HRM on employees based on AI algorithmic features

Zhou,

Wang,

Chen

2023

JOCM

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PurposeAI is an emerging tool in HRM practices that has drawn increasing attention from HRM researchers and HRM practitioners. While there is little doubt that AI-enabled HRM exerts positive effects, it also triggers negative influences. Gaining a better understanding of the dark side of AI-enabled HRM holds great significance for managerial implementation and for enriching related theoretical research.Design/methodology/approachIn this study, the authors conducted a systematic review of the published literature in the field of AI-enabled HRM. The systematic literature review enabled the authors to critically analyze, synthesize and profile existing research on the covered topics using transparent and easily reproducible procedures.FindingsIn this study, the authors used AI algorithmic features (comprehensiveness, instantaneity and opacity) as the main focus to elaborate on the negative effects of AI-enabled HRM. Drawing from inconsistent literature, the authors distinguished between two concepts of AI algorithmic comprehensiveness: comprehensive analysis and comprehensive data collection. The authors also differentiated instantaneity into instantaneous intervention and instantaneous interaction. Opacity was also delineated: hard-to-understand and hard-to-observe. For each algorithmic feature, this study connected organizational behavior theory to AI-enabled HRM research and elaborated on the potential theoretical mechanism of AI-enabled HRM's negative effects on employees.Originality/valueBuilding upon the identified secondary dimensions of AI algorithmic features, the authors elaborate on the potential theoretical mechanism behind the negative effects of AI-enabled HRM on employees. This elaboration establishes a robust theoretical foundation for advancing research in AI-enable HRM. Furthermore, the authors discuss future research directions.

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“…In a similar vein, Köchling and Wehner (2022) investigate ways to improve reactions toward AI-supported selection systems by another important stakeholder in personnel selection: applicants. In their study, they presented participants with either an AI-based selection without any information, with written information, or with video information.…”

Section: Technologymentioning

confidence: 99%

“…The fifth study compares trust, trustworthiness, and trusting behavior for different types of decision‐support (automated, human, and hybrid) across two assessment contexts (personnel selection and bonus payments) and examines trust violations (Kares et al, 2023). The sixth study investigates ways to alter applicant reactions to AI‐supported selection processes (Köchling & Wehner, 2022). Two common topics that emerge across the abstracts are (1) the need to avoid or mitigate negative stereotypes in personnel selection and (2) the use of technology, including machine learning and automation, to improve selection processes.”…”

Section: Summary Of the Papers Included In The Special Issuementioning

confidence: 99%

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Diversity and technology—Challenges for the next decade in personnel selection

Langer

Roulin

Oostrom

2023

Int J Selection Assessment

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The world of work is changing quickly. An aging workforce, largescale population migration, the democratization of remote work following the COVID-19 pandemic, and technological advancements, currently most famously driven by advances in artificial intelligence (AI), are just some of the driving forces of the changing world of work. Research is needed to properly understand those changes and their potential consequences on job seekers, employees, organizations, and the society more broadly. This Special Issue brings together a series of six original and insightful articles, based on research that was initially presented at the 6th small group meeting of the European Network of Selection Researchers (ENESER) organized by Martin Kleinmann and Annika Wilhelmy in Zurich in 2021. Together, these articles span two topics central to the changing world of work: Diversity and Technology. In this introductory article, we emphasize the growing importance of those two topics in relation to personnel recruitment, selection, or assessment. We then provide a brief overview of the six articles, and highlight how they contribute to enhancing our understanding of the role that diversity and technology play in various stages of the hiring process, from job applications to selection decisions. We conclude by providing suggestions about opportunities for future research in recruitment and selection that continues examining diversity and technology as well as their increasingly important overlaps.

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

Cited by 10 publications

References 85 publications

Gender equity in hiring: examining the effectiveness of a personality-based algorithm

Gender equity in hiring: examining the effectiveness of a personality-based algorithm

The dark side of AI-enabled HRM on employees based on AI algorithmic features

Diversity and technology—Challenges for the next decade in personnel selection

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