Use of Artificial Intelligence as Business Strategy in Recruitment Process and Social Perspective

Vardarlıer, Pelin; Zafer, Cem

doi:10.1007/978-3-030-29739-8_17

Cited by 37 publications

(21 citation statements)

References 9 publications

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“…For instance, Amazon set out to automate its talent search by relying on a prescriptive analytics tool to identify the top candidates based on profiles of successful hires over the past 10 years. As the data showed that the overwhelming majority of successful hires had previously been (white) men, the tool predicted that male candidates were more likely to be a good fit; thereby, the algorithm inappropriately excluded women (Hamilton & Sodeman, 2020;Purdy et al, 2019;Simbeck, 2019;Vardarlier & Zafer, 2020). As a result, Amazon had to abandon the recruitment tool (Dastin, 2018;Hamilton & Sodeman, 2020;Peeters et al, 2020).…”

Section: People Analytics Can Foster Path Dependenciesmentioning

confidence: 99%

The dark sides of people analytics: reviewing the perils for organisations and employees

Giermindl

Strich

Christ

et al. 2021

European Journal of Information Systems

102

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Technological advances in the field of artificial intelligence (AI) are heralding a new era of analytics and data-driven decision-making. Organisations increasingly rely on people analytics to optimise human resource management practices in areas such as recruitment, performance evaluation, personnel development, health and retention management. Recent progress in the field of AI and ever-increasing volumes of digital data have raised expectations and contributed to a very positive image of people analytics. However, transferring and applying the efficiencydriven logic of analytics to manage humans carries numerous risks, challenges, and ethical implications. Based on a theorising review our paper analyses perils that can emerge from the use of people analytics. By disclosing the underlying assumptions of people analytics and offering a perspective on current and future technological advancements, we identify six perils and discuss their implications for organisations and employees. Then, we illustrate how these perils may aggravate with increasing analytical power of people analytics, and we suggest directions for future research. Our theorising review contributes to information system research at the intersection of analytics, artificial intelligence, and human-algorithmic management.

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Section: People Analytics Can Foster Path Dependenciesmentioning

confidence: 99%

The dark sides of people analytics: reviewing the perils for organisations and employees

Giermindl

Strich

Christ

et al. 2021

European Journal of Information Systems

102

View full text Add to dashboard Cite

show abstract

“…3.1 Application of artificial intelligence in human resource management AI has been applied to a range of HRM practices, attracting more and more attention from researchers and practitioners (Brougham and Haar, 2018;Marler and Boudreau, 2017). For example, companies are increasingly using AI and algorithmic decisions in their hiring and selection processes for cost and efficiency reasons (Vardarlier and Zafer, 2020). In particular, machinelearning algorithms used in personnel selection procedures are a promising tool for many companies.…”

Section: Future Research Directionsmentioning

confidence: 99%

Guest editorial

Zhao

Liu

2021

CMS

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“…Organizations can utilize their HR data to make such predictions depending on predictive models that can be built for this purpose. In recent years, artificial intelligence (AI) is used in many different fields such as health, education, economy, and administration [5,6]. Recently, the prediction of employee attrition using AI has received a lot of research attention.…”

Section: Introductionmentioning

confidence: 99%

Employee Attrition Prediction Using Deep Neural Networks

et al. 2021

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Decision-making plays an essential role in the management and may represent the most important component in the planning process. Employee attrition is considered a well-known problem that needs the right decisions from the administration to preserve high qualified employees. Interestingly, artificial intelligence is utilized extensively as an efficient tool for predicting such a problem. The proposed work utilizes the deep learning technique along with some preprocessing steps to improve the prediction of employee attrition. Several factors lead to employee attrition. Such factors are analyzed to reveal their intercorrelation and to demonstrate the dominant ones. Our work was tested using the imbalanced dataset of IBM analytics, which contains 35 features for 1470 employees. To get realistic results, we derived a balanced version from the original one. Finally, cross-validation is implemented to evaluate our work precisely. Extensive experiments have been conducted to show the practical value of our work. The prediction accuracy using the original dataset is about 91%, whereas it is about 94% using a synthetic dataset.

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Use of Artificial Intelligence as Business Strategy in Recruitment Process and Social Perspective

Cited by 37 publications

References 9 publications

The dark sides of people analytics: reviewing the perils for organisations and employees

The dark sides of people analytics: reviewing the perils for organisations and employees

Guest editorial

Employee Attrition Prediction Using Deep Neural Networks

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