Cognitive biases in developing biased Artificial Intelligence recruitment system

Soleimani, Melika; Intezari, Ali; Taşkın, Nazım; Pauleen, David J.

doi:10.24251/hicss.2021.620

Cited by 13 publications

(4 citation statements)

References 30 publications

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“…AI cannot have human abilities, such as persuasion, building relationships with candidates, and convincing them to stay with you (Guenole and Feinzig 2018). Artificial intelligence is proficient at identifying talent, but activities such as assessing cultural fit still require humans to do so (Soleimani et al 2021;Upadhyay and Khandelwal 2018). It cannot understand cultural barriers and interact with another person better than humans can do (Nawaz 2019).…”

Section: Threat or Support For Hr Recruitment Positionsmentioning

confidence: 99%

Collaboration among recruiters and artificial intelligence: removing human prejudices in employment

Chen

2022

Cogn Tech Work

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In the global war for talent, traditional recruiting methods are failing to cope with the talent competition, so employers need the right recruiting tools to fill open positions. First, we explore how talent acquisition has transitioned from digital 1.0 to 3.0 (AI-enabled) as the digital tool redesigns business. The technology of artificial intelligence has facilitated the daily work of recruiters and improved recruitment efficiency. Further, the study analyzes that AI plays an important role in each stage of recruitment, such as recruitment promotion, job search, application, screening, assessment, and coordination. Next, after interviewing with AI recruitment stakeholders (recruiters, managers, and applicants), the study discusses their acceptance criteria for each recruitment stage; stakeholders also raised concerns about AI recruitment. Finally, we suggest that managers need to be concerned about the cost of AI recruitment, legal privacy, recruitment bias, and the possibility of replacing recruiters. Overall, the study answers the following questions: (1) How artificial intelligence is used in various stages of the recruitment process. (2) Stakeholder (applicants, recruiters, managers) perceptions of AI application in recruitment. (3) Suggestions for managers to adopt AI in recruitment. In general, the discussion will contribute to the study of the use of AI in recruitment, as well as providing recommendations for implementing AI recruitment in practice.

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Section: Threat or Support For Hr Recruitment Positionsmentioning

confidence: 99%

Collaboration among recruiters and artificial intelligence: removing human prejudices in employment

Chen

2022

Cogn Tech Work

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“…Because many organizations do not provide feedback to candidates, most candidates have a bad image of the company. Most job seekers, according to study, have a poor image of a company if they do not receive any feedback from employer roles [20,21]. As a result, candidates are dissatisfied when they receive a late or no response to their job applications.…”

Section: Reviewing Of the Usage Of Artificial Intelligence In The Rec...mentioning

confidence: 99%

“…Artificial intelligence or digital technologies such as chatbots, machine learning, and robot transaction automation are employed in HRM, which provides support. The following is a list of the function of artificial intelligence in human resource management [21]:…”

Section: Artificial Intelligence Trainings and Performance Evaluation...mentioning

confidence: 99%

Analysis of the Impact of Artificial Intelligence in Enhancing the Human Resource Practices

Biliavska¹,

Castanho²,

Vulević³

2022

JIMD

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Artificial intelligence, in a larger sense, refers to computers that have human intelligence-specific capabilities such as obtaining information, perceiving, seeing, thinking, and making decisions. At first glance, artificial intelligence, often known as "Artificial Intelligence" (AI) in the literature, causes everyone to associate something distinct. According to researches, the concept of artificial intelligence evokes an electro-mechanical robot replacing human beings, but everyone involved in this field is aware that there is a definite difference between human beings and machines. The aim of this article is to show the importance of using AI in today’s HR practices. In this context, one of the qualitative research designs, phenomenological research, was deemed 1appropriate for the thesis study. Because phenomenology establishes a framework for exploring subjects that aren't utterly unfamiliar but whose meaning isn't quite clear.AI-based HR apps have the ability to boost employee productivity while also assisting HR personnel in becoming educated advisers who can boost employee performance. AI-enabled HR solutions are capable of evaluating, predicting, diagnosing, and locating more powerful and capable employees.

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“…There are two drivers of biases in AI: training datasets and algorithms (Kaplan & Haenlein, 2019a;Shrestha et al, 2019). The previous empirical research finds like-me and cliché biases as two known biases in the recruitment and selection process that might happen in developing AIRS due to using HR managers' past hiring decisions as training datasets for developing AI (Soleimani et al, 2021). AI in the recruitment and selection process can be used in various areas such as reviewing a large volume of CVs, assessing candidates' personality through video interviews, and chatbots for candidates' engagement (Albert, 2019;Vedapradha et al, 2019).…”

Section: Cognitive Biases and Ai In Recruitmentmentioning

confidence: 99%

Mitigating Cognitive Biases in Developing AI-Assisted Recruitment Systems

Soleimani

Intezari

Pauleen

2021

International Journal of Knowledge Management

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Artificial Intelligence (AI) is increasingly embedded in business processes, including the Human Resource (HR) recruitment process. While AI can expedite the recruitment process, evidence from the industry, however, shows that AI-recruitment systems (AIRS) may fail to achieve unbiased decisions about applicants. There are risks of encoding biases in the datasets and algorithms of AI which lead AIRS to replicate and amplify human biases. To develop less biased AIRS, collaboration between HR managers and AI developers for training algorithms and exploring algorithmic biases is vital. Using an exploratory research design, 35 HR managers and AI developers globally were interviewed to understand the role of knowledge sharing during their collaboration in mitigating biases in AIRS. The findings show that knowledge sharing can help to mitigate biases in AIRS by informing data labeling, understanding job functions, and improving the machine learning model. Theoretical contributions and practical implications are suggested.

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Cognitive biases in developing biased Artificial Intelligence recruitment system

Cited by 13 publications

References 30 publications

Collaboration among recruiters and artificial intelligence: removing human prejudices in employment

Collaboration among recruiters and artificial intelligence: removing human prejudices in employment

Analysis of the Impact of Artificial Intelligence in Enhancing the Human Resource Practices

Mitigating Cognitive Biases in Developing AI-Assisted Recruitment Systems

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