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Intern. Journal of Profess. Bus. Review. |Miami, v. 8 | n. 6| p. 01-25 | e02089 | 2023.
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THE POWER OF ARTIFICIAL INTELLIGENCE IN RECRUITMENT: AN ANALYTICAL
REVIEW OF CURRENT AI-BASED RECRUITMENT STRATEGIES
Wael Abdulrahman Albassam
A
ARTICLE INFO
ABSTRACT
Purpose: The aim of this study is to contribute to the understanding of the power of
artificial intelligence (AI) in recruitment and to highlight the opportunities and
challenges associated with its use.
Theoretical framework: This paper provides a comprehensive analytical review of
current AI-based recruitment strategies, drawing on both academic research and
industry reports.
Design/methodology/approach: The paper critically evaluates the potential benefits
and drawbacks of using AI in recruitment and assesses the effectiveness of various
AI-based recruitment strategies.
Findings: The results indicate that AI-based recruitment strategies such as resume
screening, candidate matching, video interviewing, chatbots, predictive analytics,
gamification, virtual reality assessments, and social media screening offer significant
potential benefits for organizations, including improved efficiency, cost savings, and
better-quality hires. However, the use of AI in recruitment also raises ethical and legal
concerns, including the potential for algorithmic bias and discrimination.
Research, Practical & Social implications: The study concludes by emphasizing the
need for further research and development to ensure that AI-based recruitment
strategies are effective, unbiased, and aligned with ethical and legal standards.
Originality/value: The value of the study lies in its comprehensive exploration of AI
in recruitment, synthesizing insights from academic and industry perspectives, and
assessing the balance of potential benefits against ethical and legal concerns.
Doi: https://doi.org/10.26668/businessreview/2023.v8i6.2089
Article history:
Received 10 March 2023
Accepted 08 June 2023
Keywords:
HRM Technology;
Artificial Intelligence;
Recruitment;
AI-Based Recruitment Strategies;
Resume Screening;
Candidate Matching;
Video Interviewing;
Chatbots;
Predictive Analytics;
Gamification;
Virtual Reality Assessments;
Social Media Screening;
Ethics;
Legal Standards.
O PODER DA INTELIGÊNCIA ARTIFICIAL NO RECRUTAMENTO: UMA REVISÃO ANALÍTICA
DAS ATUAIS ESTRATÉGIAS DE RECRUTAMENTO BASEADAS EM IA
RESUMO
Objetivo: O objetivo deste estudo é contribuir para a compreensão do poder da inteligência artificial (IA) no
recrutamento e destacar as oportunidades e os desafios associados ao seu uso.
Estrutura teórica: Este documento oferece uma revisão analítica abrangente das atuais estratégias de
recrutamento baseadas em IA, com base em pesquisas acadêmicas e relatórios do setor.
Projeto/metodologia/abordagem: O artigo avalia criticamente os possíveis benefícios e desvantagens do uso da
IA no recrutamento e avalia a eficácia de várias estratégias de recrutamento baseadas em IA.
A
Master in Business Administration. Free Lancer Consultant. E-mail: waelalbassam1@gmail.com
Orcid: https://orcid.org/0009-0006-3434-9317
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Albassam, W. A. (2023)
The Power of Artificial Intelligence in Recruitment: An Analytical Review of Current AI-Based Recruitment Strategies
Conclusões: Os resultados indicam que as estratégias de recrutamento baseadas em IA, como triagem de
currículos, correspondência de candidatos, entrevistas por vídeo, chatbots, análise preditiva, gamificação,
avaliações de realidade virtual e triagem de mídia social, oferecem benefícios potenciais significativos para as
organizações, incluindo maior eficiência, economia de custos e contratações de melhor qualidade. No entanto, o
uso da IA no recrutamento também levanta preocupações éticas e legais, incluindo o potencial de preconceito e
discriminação algorítmica.
Implicações sociais, práticas e de pesquisa: O estudo conclui enfatizando a necessidade de mais pesquisa e
desenvolvimento para garantir que as estratégias de recrutamento baseadas em IA sejam eficazes, imparciais e
alinhadas com os padrões éticos e legais.
Originalidade/valor: O valor do estudo está em sua exploração abrangente da IA no recrutamento, sintetizando
as percepções das perspectivas acadêmicas e do setor e avaliando o equilíbrio entre os possíveis benefícios e as
preocupações éticas e legais.
Palavras-chave: Tecnologia de Gestão de Recursos Humanos, Inteligência Artificial, Recrutamento, Estratégias
de Recrutamento Baseadas em IA, Triagem de Currículos, Correspondência de Candidatos, Entrevistas por Vídeo,
Chatbots, Análise Preditiva, Gamificação, Avaliações de Realidade Virtual, Triagem de Mídia Social, Ética,
Normas Legais.
EL PODER DE LA INTELIGENCIA ARTIFICIAL EN LA CONTRATACIÓN: UNA REVISIÓN
ANALÍTICA DE LAS ACTUALES ESTRATEGIAS DE CONTRATACIÓN BASADAS EN LA IA
RESUMEN
Objetivo: El objetivo de este estudio es contribuir a la comprensión del poder de la inteligencia artificial (IA) en
la contratación y poner de relieve las oportunidades y los retos asociados a su uso.
Marco teórico: Este documento ofrece una revisión analítica exhaustiva de las actuales estrategias de contratación
basadas en la IA, a partir de investigaciones académicas e informes de la industria.
Diseño/metodología/enfoque: El documento evalúa de forma crítica los beneficios e inconvenientes potenciales
del uso de la IA en la contratación y evalúa la eficacia de diversas estrategias de contratación basadas en la IA.
Conclusiones: los resultados indican que las estrategias de contratación basadas en IA, como la selección de
currículos, el emparejamiento de candidatos, las entrevistas por vídeo, los chatbots, el análisis predictivo, la
gamificación, las evaluaciones de realidad virtual y la selección en redes sociales, ofrecen importantes beneficios
potenciales a las organizaciones, como el aumento de la eficiencia, el ahorro de costes y la mejora de la calidad de
las contrataciones. Sin embargo, el uso de la IA en la selección de personal también plantea problemas éticos y
legales, como el potencial sesgo algorítmico y la discriminación.
Repercusiones sociales, prácticas y de investigación: El estudio concluye subrayando la necesidad de seguir
investigando y desarrollando para garantizar que las estrategias de contratación basadas en la IA sean eficaces,
imparciales y acordes con las normas éticas y jurídicas.
Originalidad/valor: El valor de este estudio radica en su análisis exhaustivo de la IA en la contratación, que
sintetiza los puntos de vista del mundo académico y de la industria y evalúa el equilibrio entre los beneficios
potenciales y las preocupaciones éticas y jurídicas.
Palabras clave: Tecnología de Gestión de Recursos Humanos, Inteligencia Artificial, Contratación, Estrategias
de Contratación Basadas en IA, Selección de Currículos, Emparejamiento de Candidatos, Entrevistas por Vídeo,
Chatbots, Análisis Predictivo, Ludificación, Evaluaciones de Realidad Virtual, Selección en Redes Sociales, Ética,
Normas Jurídicas.
BACKGROUND
Digital transformations, predominantly focused on creating and enlarging markets,
stand at the forefront of devising innovative and adaptive economic business models. These
transformations significantly influence an organization's competitive standing and guide the
trajectory of market evolution. Connected to this, the application of information technology
(IT) in recruitment serves as a prime example of such digital transformation. IT not only
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The Power of Artificial Intelligence in Recruitment: An Analytical Review of Current AI-Based Recruitment Strategies
expedites and streamlines the hiring process but also broadens the reach to potential candidates,
ultimately making the recruitment process more efficient and competitive. Therefore, in today's
digital age, integrating information technology in recruitment processes is an indispensable
strategy for companies aiming to remain competitive and adaptive in the market (Ahmed et al.,
2023).
The use of artificial intelligence (AI) in recruitment has been on the rise in recent years,
with many organizations adopting AI-based recruitment strategies to improve their hiring
processes. AI is increasingly being used in various stages of the recruitment process, including
job postings, resume screening, candidate assessments, and even onboarding (Budhwar et al.,
2022; Ore & Sposato, 2021). The potential benefits of using AI in recruitment are significant,
including increased efficiency, reduced bias, improved candidate experience, and better hiring
outcomes. However, there are also potential drawbacks, such as the risk of algorithmic bias,
ethical concerns, and the need for human oversight.
Despite the growing interest in AI-based recruitment strategies, there is a research gap
in understanding their effectiveness and limitations. While there is some research on the topic,
much of it has focused on individual AI-based recruitment tools or techniques, rather than
providing a comprehensive review of the current state of AI in recruitment. Therefore, there is
a need for a critical review of the current AI-based recruitment strategies, which can provide
insights into their strengths and weaknesses and help identify areas for future research and
development.
While there has been some research on AI-based recruitment strategies, there is still a
lack of comprehensive reviews that can provide insights into their effectiveness and limitations.
Some studies have focused on the development and validation of individual AI-based
recruitment tools, such as machine learning algorithms for resume screening or chatbots for
candidate engagement (Chen, 2022; Hogg, 2019; Kazim et al., 2021). While these studies
provide valuable insights into the potential benefits of using AI in recruitment, they are limited
in scope and do not provide a comprehensive overview of the current state of AI in recruitment.
Other studies have focused on the ethical and legal concerns associated with using AI
in recruitment, such as the risk of bias and discrimination, privacy violations, and the need for
transparency and accountability (Hunkenschroer & Luetge, 2022; Tippins et al., 2021; Yam &
Skorburg, 2021). While these studies highlight important concerns that need to be addressed
when using AI in recruitment, they do not provide a comprehensive review of the concerns
related to each of the AI-based recruitment strategies.
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Albassam, W. A. (2023)
The Power of Artificial Intelligence in Recruitment: An Analytical Review of Current AI-Based Recruitment Strategies
Therefore, there is a need for a critical review of the current AI-based recruitment
strategies, which can provide insights into their strengths and weaknesses and help identify
areas for future research and development. Such a review can help organizations make informed
decisions about the use of AI in recruitment and ensure that they are using the most effective
and ethical AI-based recruitment strategies.
The aim of this paper is to provide a comprehensive analytical review of the current AI-
based recruitment strategies, drawing on both academic research and industry reports. This
review will critically evaluate the potential benefits and drawbacks of using AI in recruitment
and assess the effectiveness of various AI-based recruitment strategies. By doing so, this paper
aims to contribute to the understanding of the power of AI in recruitment and highlight the
opportunities and challenges associated with its use.
SIGNIFICANCE AND PURPOSE
The significance and purpose of this study are to provide a comprehensive analysis of
the current use of AI in recruitment. The paper aims to critically evaluate the potential benefits
and drawbacks of AI-based recruitment strategies, including chatbots, predictive analytics, and
machine learning algorithms. By conducting this analysis, the study intends to contribute to the
understanding of the power of AI in recruitment and highlight the opportunities and challenges
associated with its use. The literature review of the study provides an overview of the various
AI-based recruitment strategies and assesses their effectiveness in terms of recruitment
outcomes, such as candidate quality, diversity, and retention. Additionally, the study explores
ethical and legal considerations, including bias, privacy, and discrimination. Through a detailed
analytical review, the study provides a comparative analysis of the most promising AI-based
recruitment strategies, based on their strengths and weaknesses. The study also evaluates the
challenges and limitations of using AI in recruitment, such as the need for human oversight, the
risk of algorithmic bias, and the potential negative impact on candidate experience.
LITERATURE REVIEW
AI-based recruitment strategies offer the potential to streamline recruitment processes,
reduce bias, and improve hiring outcomes (Johnson et al., 2020). However, the effectiveness of
AI in recruitment depends on the specific techniques used and the context in which they are
applied. This section provides a review of the existing literature on AI-based recruitment
strategies, including their effectiveness, limitations, and ethical concerns.
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The Power of Artificial Intelligence in Recruitment: An Analytical Review of Current AI-Based Recruitment Strategies
A BRIEF HISTORY
AI has transformed many industries, and the world of human resources (HR) is no
exception. AI-based recruitment strategies have become increasingly popular over the years,
and are now used by many companies to streamline their hiring processes and improve the
quality of their hires (Lee et al., 2019).
The first signs of AI-based recruitment can be traced back to the 1990s when online job
boards and applicant tracking systems (ATS) emerged. These technologies allowed companies
to post job openings online and manage candidate applications digitally. However, these early
systems were not truly AI-based, as they relied on simple algorithms to match candidates to job
requirements based on keywords (Almajthoob et al., 2023).
It was not until the early 2000s that true AI-based recruitment strategies began to
emerge. One of the earliest examples was the use of predictive analytics to analyze candidate
data and identify patterns that could help predict which candidates were most likely to succeed
in a particular role (Cappelli et al., 2018). This approach was pioneered by companies like
Google, who used data analysis to improve their hiring processes and reduce employee turnover
(Singh et al., 2022).
Another early use of AI in recruitment was the use of chatbots to screen and pre-qualify
candidates. These bots would ask candidates a series of questions to determine whether they
met the minimum qualifications for a job, and would then either recommend them for further
consideration or reject them outright. This approach helped to automate the early stages of the
hiring process and reduce the workload of human recruiters (Gupta & Mishra, 2022).
Moreover, AI-based recruitment strategies have continued to evolve, with new
technologies such as natural language processing (NLP) and machine learning (ML) being used
to improve the accuracy and effectiveness of candidate screening and matching. For example,
some companies are now using AI-powered video interviewing tools to analyze candidates'
facial expressions, body language, and speech patterns to identify traits like confidence,
communication skills, and emotional intelligence (Zimmermann et al., 2016).
However, despite the many benefits of AI-based recruitment strategies, there are also
some concerns about their potential impact on diversity and inclusion. Some experts worry that
AI algorithms could inadvertently perpetuate biases and discrimination by favoring certain
types of candidates over others. To address these concerns, many companies are now working
to develop more transparent and ethical AI-based recruitment strategies that are designed to
promote diversity and eliminate bias.
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The Power of Artificial Intelligence in Recruitment: An Analytical Review of Current AI-Based Recruitment Strategies
TYPES OF AI-BASED RECRUITMENT STRATEGIES
Chatbots
Chatbots are a form of conversational AI that can interact with candidates and provide
them with information about the job opening and the organization. Chatbots can be used to
answer candidates' questions, schedule interviews, and provide feedback on their application.
Chatbots are particularly useful for high-volume recruitment processes, as they can handle a
large number of candidates simultaneously (Black & van Esch, 2020). Chatbots can also help
recruiters save time and resources by automating routine tasks, such as responding to frequently
asked questions (Zel & Kongar, 2020).
The effectiveness of chatbots in recruitment has been studied by several researchers.
For instance, Koivunen et al. (2022) conducted a study on the use of chatbots in the recruitment
process of several types of companies. The study found that chatbots were effective in
improving the candidate experience by providing quick and accurate responses to candidates'
queries. The study also found that chatbots reduced the workload of recruiters, enabling them
to focus on more complex tasks, such as candidate assessment and interviewing.
However, the use of chatbots in recruitment also has its limitations. One major limitation
is that chatbots may not be able to answer all of the candidates' questions or provide
personalized responses, which can lead to a poor candidate experience. Another limitation is
that chatbots may not be able to assess soft skills or other intangible factors that are important
in the recruitment process.
Predictive Analytics
Predictive analytics involves using data mining and machine learning algorithms to
identify patterns and predict future outcomes. In the context of recruitment, predictive analytics
can be used to identify the most promising candidates based on their past behavior and
performance. Predictive analytics can also be used to predict the likelihood of a candidate
accepting a job offer or leaving the company within a certain period (Mehta et al., 2013).
The effectiveness of predictive analytics in recruitment has been studied by several
researchers. For instance, Mehta et al. (2013) introduced a decision support system designed to
manage and optimize screening activities during the hiring process within large organizations.
The system aims to prioritize the efforts of human resource practitioners by identifying
candidates with a high likelihood of being of high quality, accepting a job offer, and remaining
with the organization for the long term. To achieve this, the system uses a keyword matching
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algorithm and several bipartite ranking algorithms with univariate loss trained on historical
actions to individually rank candidates along several dimensions. The individual rankings are
then aggregated to produce a single list, which is presented to the recruitment team through an
interactive portal that supports multiple filters to facilitate effective candidate identification.
The authors demonstrate the effectiveness of the system using data collected from a large
organization over several years, with business value metrics showing greater hiring yield with
fewer interviews. They also use historical pre-hire data to accurately identify candidates who
are likely to leave the organization quickly. The system has been successfully deployed in a
large, globally integrated enterprise.
However, the use of predictive analytics in recruitment also has its limitations. One
major limitation is that predictive analytics may not be able to account for all relevant factors
that affect a candidate's job performance, such as organizational fit or the ability to work in a
team. Another limitation is that predictive analytics may not be able to account for changes in
the job market or the organization's needs.
Machine Learning Algorithms
Machine learning algorithms can be used to screen resumes and identify the most
promising candidates based on specific criteria. Machine learning algorithms can be trained on
a large dataset of resumes to identify patterns and make predictions about the suitability of a
candidate for a particular job. Machine learning algorithms can also be used to identify potential
biases in the recruitment process and reduce them (Roy et al., 2020).
The effectiveness of machine learning algorithms in recruitment has been studied by
several researchers. Schleder et al. (2019) aimed to explore how machine learning algorithms
can be used to extract knowledge and insights from the massive amount of raw data generated
by recent advances in experimental and computational methods. The study focused on the
materials science field, where these methodologies are used to identify correlations and patterns
from large amounts of complex data. The authors reviewed the logical sequence of density
functional theory as the representative instance of electronic structure methods, followed by the
high-throughput approach used to generate large amounts of data. They then discussed how
data-driven strategies, including data mining, screening, and machine learning techniques, are
employed to extract knowledge from the data generated. The study found that machine learning
algorithms are effective in identifying patterns and correlations in large amounts of complex
data in the materials science field. The approaches to modern computational materials science
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The Power of Artificial Intelligence in Recruitment: An Analytical Review of Current AI-Based Recruitment Strategies
can be used to uncover complexities and design novel materials with enhanced properties.
However, the study also pointed out the present research problems, challenges, and potential
future perspectives of this new and exciting field.
EFFECTIVENESS OF AI-BASED RECRUITMENT STRATEGIES
The effectiveness of AI-based recruitment strategies has been studied by several
researchers. Johnson et al. (2020) aimed to explore the potential of electronic human resource
management (eHRM) and AI in addressing workforce challenges faced by the hospitality and
tourism industry. Specifically, the study aimed to discuss how e-recruiting, e-selection, and AI
tools can help organizations in the industry improve recruiting and selection outcomes, increase
individual retention rates, and decrease the time needed to replace employees. To achieve this,
the authors applied research on eHRM, AI, employee recruitment, and employee selection to
the hospitality and tourism industry. The study discussed how eHRM and AI can be applied to
the industry and provided insights for improving recruiting and selection outcomes. The
findings of the study suggest that eHRM and AI have the potential to transform the recruitment
and selection processes in the hospitality and tourism industry. However, the study also
highlights the importance of ensuring that the insights gained and decisions made using eHRM
and AI are well-received by employees and lead to better employee and organizational
outcomes.
Mehrotra and Khanna (2022) aimed to explore the acceptance of automation in human
resource management by employers and the extent to which recruiters can use AI to hire people.
The study aimed to provide insights into the impact of digitization on human resource functions
and processes in the ever-evolving and competitive business world. The study employed a
thematic analysis approach and collected data from primary sources by conducting semi-
structured interviews with four experts working in IT organizations. The study aimed to provide
useful insights for recruiters and HR managers to consider the fields of AI implementation and
management to take advantage of cost-cutting technical developments. The results suggested
that there is a growing acceptance of automation in human resource management by employers,
and recruiters can use AI to hire people. The study highlights the impact of digitization on
reshaping different human resource functions and processes, and the potential for AI to make
these processes more effective and customer-friendly. Overall, the study provides valuable
insights for recruiters and HR managers to consider the fields of AI implementation and
management in the context of human resource management.
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The Power of Artificial Intelligence in Recruitment: An Analytical Review of Current AI-Based Recruitment Strategies
The effectiveness of AI-based recruitment strategies, however, depends on several
factors i.e., the effectiveness of predictive analytics may depend on the quality of the data used
to train the algorithms. Predictive analytics involves the use of algorithms to analyze data and
predict outcomes. In recruitment, predictive analytics can be used to identify candidates who
are likely to perform well in a particular role. The quality of the data used to train the algorithms
is crucial to the effectiveness of predictive analytics. If the data is of poor quality, the algorithms
may produce inaccurate predictions, leading to poor hires.
Similarly, the effectiveness of chatbots may depend on the design of the chatbot and the
quality of the responses provided to candidates. Chatbots are computer programs designed to
simulate human conversation. In recruitment, chatbots can be used to answer candidates'
questions, schedule interviews, and provide feedback. The design of the chatbot is crucial to its
effectiveness. If the chatbot is poorly designed, candidates may have difficulty using it, leading
to frustration and a poor candidate experience. Similarly, the quality of the responses provided
to candidates is crucial to the effectiveness of chatbots. If the responses are inaccurate or
unhelpful, candidates may become frustrated, leading to a poor candidate experience.
LIMITATIONS OF AI-BASED RECRUITMENT STRATEGIES
Despite the potential benefits of AI-based recruitment strategies, there are also several
limitations that need to be considered. One major limitation is that AI-based recruitment
strategies may not be able to account for all relevant factors that affect a candidate's job
performance, such as their cultural fit with the organization or their ability to work in a team.
Another limitation is that AI-based recruitment strategies may perpetuate bias if they are trained
on biased data.
One of the major limitations of AI-based recruitment strategies is their inability to
account for all relevant factors that affect a candidate's job performance. For example, AI
algorithms may not be able to assess a candidate's cultural fit with the organization or their
ability to work in a team. These factors are critical for job performance but are often difficult
to quantify and measure accurately. Therefore, relying solely on AI-based recruitment
strategies may lead to overlooking highly qualified candidates who do not meet the
predetermined criteria set by the algorithm.
Another significant limitation of AI-based recruitment strategies is the potential to
perpetuate bias if they are trained on biased data. AI algorithms are only as unbiased as the data
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The Power of Artificial Intelligence in Recruitment: An Analytical Review of Current AI-Based Recruitment Strategies
they are trained on. If the data used to train the AI algorithm is biased, the algorithm will
perpetuate the same biases when selecting candidates.
Gupta et al. (2021) aimed to investigate the extent to which individuals question AI-
based recommendations when perceived as biased, specifically focusing on the effects of
espoused national cultural values on AI questionability. This study was motivated by concerns
about the devastating effects of recommender systems on society's vulnerable and marginalized
communities, particularly with regards to perpetuating and exacerbating racial and gender
biases. To address this gap in knowledge, the researchers collected data from 387 survey
respondents in the United States and examined how cultural values associated with
collectivism, masculinity, and uncertainty avoidance might influence individuals' tendency to
question biased AI-based recommendations. The methodology employed in this study was a
quantitative survey approach, which included questions about cultural values and AI
questionability. The findings of this study suggest that individuals with espoused national
cultural values associated with collectivism, masculinity, and uncertainty avoidance are more
likely to question biased AI-based recommendations. These results contribute to the current
academic discourse about the need to hold AI accountable and advance current understanding
of how cultural values affect AI questionability due to perceived bias. Overall, this study sheds
light on the complex interplay between cultural values and AI questionability and provides
important insights for policymakers and developers in the AI industry.
To prevent the perpetuation of bias, organizations need to ensure that the data used to
train AI algorithms is diverse and representative of the population they wish to hire from.
Additionally, they need to monitor the performance of AI-based recruitment strategies to ensure
that they are not perpetuating bias.
ETHICAL CONCERNS
While AI-based recruitment strategies offer many benefits, organizations must be aware
of the ethical concerns associated with their use. By taking proactive steps to ensure privacy
and fairness, organizations can leverage AI technology to enhance their recruitment processes
while minimizing potential risks and ethical concerns (Hunkenschroer & Luetge, 2022). AI-
based recruitment strategies often rely on collecting and analyzing large amounts of personal
data, including candidates' names, addresses, work history, and other sensitive information.
This data can be vulnerable to cyberattacks or misuse, and organizations must take measures to
ensure that the data is securely stored and protected from unauthorized access (Du & Xie, 2020).
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The Power of Artificial Intelligence in Recruitment: An Analytical Review of Current AI-Based Recruitment Strategies
Moreover, candidates have the right to know what information is being collected about
them and how it is being used. Therefore, organizations must be transparent about their data
collection processes and provide candidates with clear information about their privacy policies
and data protection measures.
Another ethical concern related to AI-based recruitment strategies is fairness. AI
algorithms can be programmed with inherent biases that may perpetuate discrimination or
unfairness against certain groups of candidates. For instance, if an AI algorithm is trained on
data that contains biased information, such as past hiring decisions based on gender or ethnicity,
the algorithm may replicate these biases in its hiring decisions (Wei & Zhou, 2022; Sari et al.,
2023).
To prevent these issues, organizations must ensure that their recruitment processes are
fair and unbiased. This means that AI algorithms should be trained on diverse and representative
data sets to avoid perpetuating biases, and the recruitment team should regularly review the
algorithm's output to detect and correct any biases.
Analysis of AI-based Recruitment Strategies
AI-based recruitment strategies include a range of approaches that use machine learning
algorithms and natural language processing to automate various stages of the recruitment
process. These strategies aim to reduce bias, improve efficiency, and enhance the overall
effectiveness of the recruitment process. Some of the most promising AI-based recruitment
strategies are discussed below.
RESUME SCREENING
Resume screening is an essential part of the recruitment process that involves reviewing
resumes and identifying potential candidates who possess the required qualifications and skills
for a job. However, this can be a time-consuming and daunting task, especially for large
organizations that receive hundreds or thousands of resumes for a single position (Derous &
Ryan, 2018). To address this challenge, many companies are turning to AI-powered resume
screening tools to automate the process and save time (Vedapradha et al., 2019).
AI algorithms used for resume screening typically work by analyzing resumes against a
set of predefined criteria such as job requirements, qualifications, and skills (Hunkenschroer &
Luetge, 2022). These algorithms can quickly and accurately identify candidates who match the
criteria, allowing recruiters to focus on the most suitable candidates and significantly reducing
the time and effort required for manual screening.
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The Power of Artificial Intelligence in Recruitment: An Analytical Review of Current AI-Based Recruitment Strategies
Despite the many benefits of AI-based resume screening, there are also potential risks
that need to be addressed. One of the most significant challenges is the potential for algorithmic
bias, where the algorithm may unintentionally discriminate against certain candidates based on
factors such as gender, race, or age (Fu et al., 2020; Yarger et al., 2019). For instance, if the
algorithm is trained on biased data or criteria, it may wrongly exclude qualified candidates from
the selection pool.
To reduce the risk of algorithmic bias, it is crucial to ensure that the algorithm is
designed and trained with unbiased data and criteria. This can be achieved by removing any
potentially biased language or criteria from the job description and using diverse datasets that
represent a range of backgrounds and experiences. Additionally, human oversight is essential
to ensure fair and unbiased decision-making, and recruiters should regularly review and
evaluate the algorithm's performance to detect and address any biases.
CANDIDATE MATCHING
Candidate matching refers to the use of machine learning algorithms to analyze large
datasets and identify the best-suited candidates for a job based on their qualifications, skills,
and experience (Cardoso et al., 2021). This approach aims to streamline the recruitment process
by reducing the time and effort required to identify suitable candidates, and improve the
accuracy of the selection process. Several types of candidates matching algorithms exist, each
with its own strengths and limitations. For example, some algorithms use natural language
processing (NLP) techniques to extract relevant information from resumes or job descriptions,
while others use predictive analytics to identify high-potential candidates based on their past
performance or other relevant data points (Soni et al., 2020).
Despite the potential benefits of candidate matching, the quality of the data used to train
the algorithm is crucial to ensure accurate results. Poor-quality data or biased training sets can
lead to inaccurate candidate recommendations, which could ultimately harm the hiring process
(Harsha et al., 2022). As a result, it is important to ensure that the algorithm is trained on diverse
and representative datasets to minimize the risk of bias or discrimination. Moreover, there is a
risk that the candidate matching algorithm may prioritize certain skills or qualifications over
others, leading to bias and discrimination. For example, if the algorithm is trained to prioritize
candidates who have attended prestigious universities or who have certain certifications, it may
overlook candidates who have equivalent skills and qualifications but who do not fit these
criteria.
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The Power of Artificial Intelligence in Recruitment: An Analytical Review of Current AI-Based Recruitment Strategies
VIDEO INTERVIEW
Video interview analysis is an AI-based recruitment technique that has gained
increasing attention in recent years. The method involves analyzing video interviews of job
candidates using natural language processing and facial recognition algorithms to evaluate their
suitability for the job (Dunlop et al., 2022). One of the advantages of video interview analysis
is that it provides valuable insights into a candidate's communication skills, personality, and
cultural fit, which are difficult to assess through traditional interviews. For example, facial
recognition algorithms can detect a candidate's emotional expressions, eye contact, and body
language, providing recruiters with non-verbal cues that can help evaluate the candidate's
communication skills (Hemamou et al., 2019). Similarly, natural language processing
algorithms can evaluate the candidate's spoken responses and provide insights into their
language proficiency, grammar, and vocabulary usage (Kadyan et al., 2021).
However, the use of facial recognition technology raises significant privacy concerns.
Some critics argue that the use of facial recognition in recruitment could lead to discrimination
and bias against certain groups, such as people of color or those with disabilities (Andrejevic
& Selwyn, 2019). Moreover, facial recognition technology is not always accurate, and it can be
prone to errors and misidentification, leading to potential wrongful decisions (Wang et al.,
2023).
Another potential limitation of video interview analysis is that the accuracy of natural
language processing algorithms can be affected by accent and dialect variations. This means
that candidates who have a non-standard accent or dialect may be unfairly penalized, leading
to potential bias and discrimination in the recruitment process.
CHATBOTS
AI-based chatbots have been widely used in recruitment to automate various aspects of
the hiring process. They can assist recruiters in responding to candidates' queries in real-time,
provide information about the job vacancies, and even guide candidates through the application
process. Chatbots can be integrated with multiple communication channels such as messaging
apps, emails, and social media platforms, which make it easier for candidates to connect with
recruiters (Suen & Hung, 2023).
The benefits of using chatbots in recruitment are numerous. Firstly, chatbots can provide
24/7 support to candidates, which can significantly improve candidate engagement. This means
that candidates can receive assistance at any time of the day, and they do not have to wait for
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The Power of Artificial Intelligence in Recruitment: An Analytical Review of Current AI-Based Recruitment Strategies
the recruiter to be available. Secondly, chatbots can reduce the workload of recruiters by
automating repetitive tasks such as scheduling interviews and responding to frequently asked
questions. This allows recruiters to focus on more critical tasks such as sourcing and evaluating
candidates (Gigi & Gunaseeli, 2021).
Moreover, chatbots can also help in the initial screening of candidates. By asking pre-
determined questions, chatbots can identify the most suitable candidates for the job and rank
them accordingly. This can save recruiters a lot of time and effort in the initial stages of the
recruitment process (Swapna & Arpana, 2021). However, it is important to note that chatbots
are not a replacement for human recruiters. They are designed to assist recruiters and improve
the candidate experience, but they cannot replace the human touch that is needed in recruitment.
Candidates still value human interaction and personalized communication, especially during
the later stages of the recruitment process.
PREDICTIVE ANALYTICS
Predictive analytics is a technique used in AI that involves the use of statistical
algorithms and machine learning models to analyze data and make predictions about future
outcomes. In the context of recruitment, predictive analytics can be used to analyze historical
recruitment data to identify patterns and predict future hiring needs (Kakulapati et al., 2020).
By analyzing past recruitment data, recruiters can gain insights into which sources have
historically yielded the most qualified candidates, which can help them prioritize their
recruitment efforts. For example, if past data shows that a particular job board consistently
yields a high number of qualified candidates, recruiters can focus their advertising efforts on
that job board to maximize their chances of finding the right candidate.
Predictive analytics can also be used to predict future hiring needs. By analyzing past
hiring patterns and business growth projections, recruiters can forecast the number of hires they
will need to make in the future and plan their recruitment activities accordingly. This can help
recruiters ensure that they have enough resources in place to manage the recruitment process
and avoid being caught off guard by sudden hiring needs (IBM, 2022).
One study conducted by The Society for Human Resource Management (SHRM) found
that the use of predictive analytics in recruitment can significantly improve recruitment
outcomes, including increased candidate quality and reduced time-to-fill positions. Another
study conducted by HR Future found that predictive analytics can also help reduce recruitment
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The Power of Artificial Intelligence in Recruitment: An Analytical Review of Current AI-Based Recruitment Strategies
bias by focusing on objective data rather than subjective factors such as candidate age, gender,
or ethnicity (Panayides, 2023).
GAMIFICATION
Gamification is a popular AI-based recruitment strategy that involves the use of game
elements to enhance the recruitment process. According to the study conducted by Tansley et
al. (2016), gamification has emerged as a powerful tool to improve candidate engagement and
to provide insights into candidates' skills and abilities. The use of points, badges, and
leaderboards in the recruitment process can create a sense of competition among candidates,
which can motivate them to perform better. Moreover, gamification can help organizations to
attract and retain top talent; can enhance user engagement and motivation, leading to a more
positive user experience. In the context of recruitment, this can help organizations to create a
positive brand image and attract more candidates (Ērgle & Ludviga, 2018).
VIRTUAL REALITY (VR)
Virtual Reality (VR) Assessments have emerged as a new tool in AI-based recruitment
strategies, providing recruiters with an innovative and immersive way to evaluate job
candidates' technical and practical skills. VR assessments use simulated environments to
evaluate candidates' performance in various job-related tasks, enabling recruiters to measure
candidates' abilities and aptitude in real-world scenarios (Guichet et al., 2022).
According Guichet et al. (2022), more than half of the surveyed companies indicated
that they were exploring VR assessments as a way to enhance their recruitment process. One
of the key benefits of VR assessments is their ability to reduce the time and costs associated
with traditional in-person assessments. For example, VR assessments can eliminate the need
for expensive equipment, travel costs, and on-site testing facilities. Additionally, VR
assessments can be accessed remotely, making it easier for recruiters to evaluate candidates
from different locations.
Nevertheless, there are some limitations to using VR assessments in recruitment. For
example, candidates may not have access to the necessary equipment to participate in the
assessment, or they may not be comfortable using VR technology. Additionally, VR
assessments may not be suitable for all job roles and industries, such as those that require
physical interaction or face-to-face communication.
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The Power of Artificial Intelligence in Recruitment: An Analytical Review of Current AI-Based Recruitment Strategies
SOCIAL MEDIA SCREENING
Social media screening involves analyzing candidates' social media profiles to identify
their interests, personality, and values. Social media screening can provide valuable insights
into candidates' suitability for a job and their cultural fit with the organization (Jeske & Shultz,
2015). For example, if a candidate's social media profile indicates that they are passionate about
a particular cause, it may suggest that they would be a good fit for a non-profit organization
that shares that cause.
In the same context, there are also risks associated with social media screening. Firstly,
it can raise privacy concerns. Candidates may not be comfortable with their personal
information being used to make hiring decisions. Secondly, social media screening can
introduce bias into the recruitment process. If recruiters base their hiring decisions on factors
such as a candidate's race, gender, or religion, it can lead to discrimination. To mitigate these
risks, organizations should establish clear guidelines for social media screening. For example,
they should only screen candidates' public social media profiles and not request login
information. They should also ensure that the information they use to make hiring decisions is
job-related and does not discriminate against protected classes. Additionally, organizations
should provide candidates with the opportunity to review and dispute any information obtained
through social media screening.
Comparison of AI-Based Recruitment Techniques and their Suitability for Different
Types of Organizations and Job Roles
While each of the AI-based recruitment techniques discussed above offers unique
advantages, their suitability varies depending on the organization's size, industry, and job roles
(Table 1).
Table 1. Comparison of AI-Based Recruitment Techniques and their Suitability for Different Types of
Organizations and Job Roles
Recruitment
Technique
Suitability for Large
Organizations
Suitability for Small
Organizations
Suitability for Different Job Roles
Resume
Screening
High
High
Suitable for roles with clearly defined
criteria and qualifications
Candidate
Matching
High
High
Suitable for roles with high volume of
applicants and data
Video Interview
High
High
Suitable for roles with strong
communication and interpersonal
skills
Chatbots
High
High
Suitable for roles with frequent
communication with candidates
Predictive
Analytics
High
High
Suitable for roles with clearly defined
metrics and KPIs
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The Power of Artificial Intelligence in Recruitment: An Analytical Review of Current AI-Based Recruitment Strategies
Gamification
Moderate
Moderate
Suitable for roles with strong
motivation and goal orientation
Virtual Reality
(VR)
High
High
Suitable for technical and practical
skills assessment
Social Media
Screening
Moderate
Moderate
Suitable for roles with strong
alignment with company culture and
values
Source: Prepared by the author (2023).
Resume screening and candidate matching are two of the most common AI-based
recruitment techniques used by organizations of all sizes and across all industries. These
techniques are particularly suitable for organizations that receive a high volume of job
applications or have large recruitment teams. Resume screening tools can significantly reduce
the time and effort required for manual screening, enabling recruiters to focus on the most
suitable candidates. Candidate matching algorithms can analyze vast datasets and identify the
best-suited candidates for a job based on their qualifications, skills, and experience. These
techniques are particularly useful for organizations with complex hiring needs, such as those in
the technology or healthcare industries.
Video interview analysis and virtual reality assessments are more advanced AI-based
recruitment techniques that are suitable for organizations looking to enhance their candidate
evaluation process. Video interview analysis provides valuable insights into a candidate's
communication skills, personality, and cultural fit, while virtual reality assessments evaluate
candidates' technical and practical skills in real-world scenarios. These techniques are
particularly useful for organizations in industries that require specialized technical skills, such
as engineering or software development.
Chatbots are a popular AI-based recruitment technique that can provide 24/7 support to
candidates, automate repetitive tasks, and assist with initial candidate screening. These tools
are particularly useful for small to medium-sized organizations with limited recruitment
resources or those looking to improve candidate engagement.
Predictive analytics is an AI-based recruitment technique that is particularly suitable for
large organizations with vast amounts of recruitment data. By analyzing past recruitment data,
recruiters can gain insights into which sources have historically yielded the most qualified
candidates, forecast future hiring needs, and plan recruitment activities accordingly. This
technique is particularly useful for organizations in rapidly growing industries such as
technology or healthcare.
Gamification is an emerging AI-based recruitment technique that aims to improve
candidate engagement and provide insights into candidates' skills and abilities. This technique
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The Power of Artificial Intelligence in Recruitment: An Analytical Review of Current AI-Based Recruitment Strategies
is particularly useful for organizations looking to attract and retain top talent, such as those in
the creative or marketing industries. However, the use of gamification in recruitment should be
approached with caution to prevent bias and discrimination.
Social media screening is an AI-based recruitment technique that can provide valuable
insights into a candidate's interests, personality, and values. However, the use of social media
screening can raise privacy concerns and introduce bias into the recruitment process. This
technique is particularly suitable for organizations in industries that require specific values or
interests, such as non-profit organizations.
The suitability of AI-based recruitment techniques varies depending on the
organization's size, industry, and job roles. While each technique offers unique advantages, it's
essential to approach their use with caution to prevent bias and discrimination. Organizations
should carefully evaluate their recruitment needs and select the techniques that best meet those
needs while ensuring fairness and inclusivity throughout the recruitment process.
However, it is also essential to note that the use of AI-based recruitment techniques
should not replace the human touch that is necessary for a successful recruitment process.
Candidates still value human interaction and personalized communication, especially during
the later stages of the recruitment process. Therefore, organizations should balance the use of
AI-based recruitment techniques with personalized communication and human interaction to
ensure that the recruitment process is fair, efficient, and effective.
Moreover, it is important to consider the potential limitations and risks associated with
each AI-based recruitment technique. For example, the use of facial recognition technology in
video interview analysis can raise significant privacy concerns and potential bias against certain
groups. Similarly, social media screening can introduce bias into the recruitment process if
recruiters base their hiring decisions on factors such as a candidate's race, gender, or religion.
Therefore, organizations should establish clear guidelines and best practices for the use of AI-
based recruitment techniques, such as only using job-related information and data and
providing candidates with the opportunity to review and dispute any information obtained
through social media screening.
Additionally, organizations should regularly evaluate the performance of AI-based
recruitment techniques and adjust them as needed to ensure that they meet their recruitment
needs effectively. For example, if a candidate matching algorithm is prioritizing certain skills
or qualifications over others, recruiters should adjust the algorithm to ensure that it doesn't
inadvertently favor certain groups of candidates over others.
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The Power of Artificial Intelligence in Recruitment: An Analytical Review of Current AI-Based Recruitment Strategies
In conclusion, while the use of AI-based recruitment techniques can streamline and
enhance the recruitment process, organizations should approach their use with caution and
ensure that they prioritize fairness, inclusivity, and human interaction. The suitability of each
technique varies depending on the organization's size, industry, and job roles, and organizations
should carefully evaluate their recruitment needs before selecting the techniques that best meet
those needs. Additionally, organizations should regularly evaluate the performance of AI-based
recruitment techniques and adjust them as needed to ensure that they meet their recruitment
needs effectively while also promoting fairness and inclusivity in the recruitment process.
CHALLENGES AND LIMITATIONS OF USING AI IN RECRUITMENT
The use of AI in recruitment has the potential to revolutionize the hiring process by
reducing bias, improving efficiency, and enhancing the overall effectiveness of the recruitment
process. However, there are several challenges and limitations that organizations need to
consider when implementing AI-based recruitment strategies. One of the most significant
challenges is the need for human oversight. While AI can automate various stages of the
recruitment process, it cannot replace the human touch that is needed to make final hiring
decisions. Human oversight is essential to ensure that the AI algorithms are making fair and
unbiased decisions and that candidates are being evaluated based on their qualifications and
skills rather than subjective factors.
Another challenge is the risk of algorithmic bias. AI algorithms are only as unbiased as
the data and criteria used to train them. If the data or criteria used to train the algorithm are
biased, it can lead to unfair and discriminatory hiring practices. Therefore, it is crucial to ensure
that AI algorithms are designed and trained with unbiased data and criteria and that they are
regularly reviewed and evaluated to detect and address any biases.
Additionally, the use of AI in recruitment can potentially negatively impact the
candidate experience. Candidates may feel that they are being evaluated solely based on their
resume or social media profile and not given a fair chance to showcase their skills and abilities
during the interview process. This can lead to a negative perception of the organization and
may discourage qualified candidates from applying for future job openings.
Furthermore, the use of AI in recruitment can raise significant privacy concerns,
especially when using technologies such as facial recognition or social media screening.
Candidates may not be comfortable with their personal information being used to make hiring
decisions, and it can potentially lead to discrimination or bias.
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The Power of Artificial Intelligence in Recruitment: An Analytical Review of Current AI-Based Recruitment Strategies
Finally, while AI-based recruitment strategies have the potential to transform the
recruitment process, it is essential to use them carefully and thoughtfully to mitigate potential
challenges and limitations. Organizations must ensure that they have human oversight in the
hiring process, and that AI algorithms are designed and trained with unbiased data and criteria.
Additionally, it is important to prioritize candidate experience and privacy to maintain a
positive perception of the organization and attract top talent.
CONCLUSION
In recapitulating this study's objective, we sought to explore the power of AI in
recruitment, emphasizing its potential benefits and drawbacks, particularly in the context of
emerging AI-based recruitment strategies. This comprehensive analysis was carried out with
the purpose of contributing to an understanding of AI's capabilities in recruitment and
elucidating the opportunities and challenges inherent to its use.
Our findings revealed that AI has the capacity to revolutionize recruitment processes.
AI-based strategies such as resume screening, candidate matching, video interviewing,
chatbots, predictive analytics, gamification, virtual reality assessments, and social media
screening are not only innovating traditional approaches to recruitment but are also offering
significant benefits. These include improved efficiency, cost savings, and higher-quality hires,
which can tremendously enhance organizational effectiveness.
However, this revolution is not without its challenges. The study identified the need for
human oversight in the recruitment process to mitigate AI's limitations, especially regarding
the risk of algorithmic bias. It also flagged privacy concerns and potential negative impacts on
the candidate experience, which could be exacerbated by the use of AI.
The limitations of the study primarily relate to its scope and the rapidly evolving nature
of AI technology. Given the dynamic and complex nature of AI, it is essential to acknowledge
that the effectiveness and potential issues associated with AI-based recruitment strategies are
likely to change over time as technology advances. The rapid pace of change in AI technologies
and the evolving legal and ethical landscape surrounding its use may also limit the longevity of
some of the conclusions drawn in this study.
In light of these findings and the identified limitations, future research is recommended
in several areas. Firstly, as AI technologies continue to evolve and diversify, there is a need for
ongoing research into their application in recruitment processes. This should include not only
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the further investigation of existing AI-based recruitment strategies but also the exploration of
emerging technologies and their potential impacts.
Secondly, more in-depth investigation into the ethical and legal considerations of AI in
recruitment is warranted. Future work should focus on developing strategies to minimize bias
and discrimination in AI algorithms, while also exploring how to ensure candidate privacy is
maintained and legal requirements are met.
Thirdly, it will be beneficial to conduct empirical research into the impact of AI on the
candidate experience. This would involve exploring candidate perceptions of AI in recruitment
processes and investigating how AI might enhance rather than detract from the candidate
experience.
Lastly, future research should focus on the skills and competencies needed by HR
professionals to leverage AI in recruitment. As AI continues to transform recruitment processes,
it will be necessary for HR professionals to develop new knowledge and skills to effectively
harness the power of these technologies.
In conclusion, while AI holds great promise for the field of recruitment, it is essential
to approach its adoption thoughtfully, considering both its potential benefits and associated
challenges. With further research, development, and thoughtful implementation, AI could play
a significant role in shaping the future of recruitment, helping organizations attract, engage, and
hire top talent while ensuring fairness, inclusivity, and respect for candidate privacy.
REFERENCES
Ahmed, D. A., Ibrahim, M. A., & Saeed, Y. J. (2023). The Role of Information Management
Systems in the Implementation of the Digital Economy Development Strategy. International
Journal of Professional Business Review, 8(5), e01419–e01419.
https://doi.org/10.26668/businessreview/2023.v8i5.1419
Almajthoob, A. M. H., Hamdan, A., & Hakami, H. (2023). The Effectiveness of Applying
Artificial Intelligence in Recruitment in Private Sectors. Digitalisation: Opportunities and
Challenges for Business, 631–641. https://doi.org/10.1007/978-3-031-26953-0_58
Andrejevic, M., & Selwyn, N. (2019). Facial recognition technology in schools: critical
questions and concerns. Learning, Media and Technology, 45(2), 1–14.
https://doi.org/10.1080/17439884.2020.1686014
Black, J. S., & van Esch, P. (2020). AI-enabled recruiting: What is it and how should a manager
use it? Business Horizons, 63(2), 215–226. https://doi.org/10.1016/j.bushor.2019.12.001
Budhwar, P., Malik, A., De Silva, M. T. T., & Thevisuthan, P. (2022). Artificial intelligence –
challenges and opportunities for international HRM: a review and research agenda. The
Intern. Journal of Profess. Bus. Review. | Miami, v. 8 | n. 5 | p. 01-25 | e02089 | 2023.
22
Albassam, W. A. (2023)
The Power of Artificial Intelligence in Recruitment: An Analytical Review of Current AI-Based Recruitment Strategies
International Journal of Human Resource Management, 33(6), 1065–1097. tandfonline.
https://doi.org/10.1080/09585192.2022.2035161
Cappelli, P., Tambe, P., & Yakubovich, V. (2018). Artificial Intelligence in Human Resources
Management: Challenges and a Path Forward. SSRN Electronic Journal, 61(4).
https://doi.org/10.2139/ssrn.3263878
Cardoso, A., Mourão, F., & Rocha, L. (2021). The matching scarcity problem: When
recommenders do not connect the edges in recruitment services. Expert Systems with
Applications, 175, 114764. https://doi.org/10.1016/j.eswa.2021.114764
Chen, Z. (2022). Collaboration among recruiters and artificial intelligence: removing human
prejudices in employment. Cognition, Technology & Work. https://doi.org/10.1007/s10111-
022-00716-0
Derous, E., & Ryan, A. M. (2018). When your resume is (not) turning you down: Modelling
ethnic bias in resume screening. Human Resource Management Journal, 29(2), 113–130.
https://doi.org/10.1111/1748-8583.12217
Du, S., & Xie, C. (2020). Paradoxes of Artificial Intelligence in Consumer markets: Ethical
Challenges and Opportunities. Journal of Business Research, 129.
https://doi.org/10.1016/j.jbusres.2020.08.024
Dunlop, P. D., Holtrop, D., & Wee, S. (2022). How asynchronous video interviews are used in
practice: A study of an Australian‐based AVI vendor. International Journal of Selection and
Assessment, 30(3). https://doi.org/10.1111/ijsa.12372
Ērgle, D., & Ludviga, I. (2018). USE OF GAMIFICATION IN HUMAN RESOURCE
MANAGEMENT: IMPACT ON ENGAGEMENT AND SATISFACTION. 10th International
Scientific Conference “Business and Management 2018.” https://doi.org/10.3846/bm.2018.45
Fu, R., Huang, Y., & Singh, P. V. (2020). Artificial Intelligence and Algorithmic Bias: Source,
Detection, Mitigation, and Implications. Pushing the Boundaries: Frontiers in Impactful
OR/OM Research, 39–63. https://doi.org/10.1287/educ.2020.0215
Gigi, G., & Gunaseeli, P. (2021). HR Recruitment Through Chatbot- An Innovative Approach.
Journal of Contemporary Issues in Business and Government, 26(02).
https://doi.org/10.47750/cibg.2020.26.02.075
Guichet, P. L., Huang, J., Zhan, C., Millet, A., Kulkarni, K., Chhor, C., Mercado, C., &
Fefferman, N. (2022). Incorporation of a Social Virtual Reality Platform into the Residency
Recruitment Season. Academic Radiology, 29(6), 935–942.
https://doi.org/10.1016/j.acra.2021.05.024
Gupta, A., & Mishra, M. (2022). Ethical Concerns While Using Artificial Intelligence in
Recruitment of Employees. Business Ethics and Leadership, 6(2), 6–11.
https://doi.org/10.21272/bel.6(2).6-11.2022
Gupta, M., Parra, C. M., & Dennehy, D. (2021). Questioning Racial and Gender Bias in AI-
based Recommendations: Do Espoused National Cultural Values Matter? Information Systems
Frontiers. https://doi.org/10.1007/s10796-021-10156-2
Intern. Journal of Profess. Bus. Review. | Miami, v. 8 | n. 5 | p. 01-25 | e02089 | 2023.
23
Albassam, W. A. (2023)
The Power of Artificial Intelligence in Recruitment: An Analytical Review of Current AI-Based Recruitment Strategies
Harsha, T. M., Moukthika, G. S., Sai, D. S., Pravallika, M. N. R., Anamalamudi, S., & Enduri,
M. (2022). Automated Resume Screener using Natural Language Processing(NLP). 2022 6th
International Conference on Trends in Electronics and Informatics (ICOEI).
https://doi.org/10.1109/icoei53556.2022.9777194
Hemamou, L., Felhi, G., Martin, J.-C., & Clavel, C. (2019). Slices of Attention in
Asynchronous Video Job Interviews. 2019 8th International Conference on Affective
Computing and Intelligent Interaction (ACII). https://doi.org/10.1109/acii.2019.8925439
Hogg, P. (2019). Artificial intelligence: HR friend or foe? Strategic HR Review, 18(2), 47–51.
https://doi.org/10.1108/shr-11-2018-0094
Hunkenschroer, A. L., & Luetge, C. (2022). Ethics of AI-Enabled Recruiting and Selection: A
Review and Research Agenda. Journal of Business Ethics, 178(4).
https://doi.org/10.1007/s10551-022-05049-6
IBM. (2022). What Is Predictive Analytics? | IBM. Www.ibm.com.
https://www.ibm.com/topics/predictive-analytics
Jeske, D., & Shultz, K. S. (2015). Using social media content for screening in recruitment and
selection: pros and cons. Work, Employment and Society, 30(3), 535–546.
https://doi.org/10.1177/0950017015613746
Johnson, R. D., Stone, D. L., & Lukaszewski, K. M. (2020). The benefits of eHRM and AI for
talent acquisition. Journal of Tourism Futures, ahead-of-print(ahead-of-print).
https://doi.org/10.1108/jtf-02-2020-0013
Kadyan, V., Singh, A., Mittal, M., & Abualigah, L. (2021). Deep Learning Approaches for
Spoken and Natural Language Processing. Springer International Publishing : Imprint:
Springer.
Kakulapati, V., Chaitanya, K. K., Chaitanya, K. V. G., & Akshay, P. (2020). Predictive
analytics of HR - A machine learning approach. Journal of Statistics and Management Systems,
23(6), 959–969. https://doi.org/10.1080/09720510.2020.1799497
Kazim, E., Koshiyama, A. S., Hilliard, A., & Polle, R. (2021). Systematizing Audit in
Algorithmic Recruitment. Journal of Intelligence, 9(3), 46.
https://doi.org/10.3390/jintelligence9030046
Koivunen, S., Ala-Luopa, S., Olsson, T., & Haapakorpi, A. (2022). The March of Chatbots into
Recruitment: Recruiters’ Experiences, Expectations, and Design Opportunities. Computer
Supported Cooperative Work (CSCW). https://doi.org/10.1007/s10606-022-09429-4
Lee, J., Suh, T., Roy, D., & Baucus, M. (2019). Emerging Technology and Business Model
Innovation: The Case of Artificial Intelligence. Journal of Open Innovation: Technology,
Market, and Complexity, 5(3), 44. https://doi.org/10.3390/joitmc5030044
Mehrotra, S., & Khanna, A. (2022). Recruitment Through AI in Selected Indian Companies.
Metamorphosis: A Journal of Management Research, 097262252110662.
https://doi.org/10.1177/09726225211066220
Intern. Journal of Profess. Bus. Review. | Miami, v. 8 | n. 5 | p. 01-25 | e02089 | 2023.
24
Albassam, W. A. (2023)
The Power of Artificial Intelligence in Recruitment: An Analytical Review of Current AI-Based Recruitment Strategies
Mehta, S., Pimplikar, R., Singh, A., Varshney, L. R., & Visweswariah, K. (2013). Efficient
multifaceted screening of job applicants. Proceedings of the 16th International Conference on
Extending Database Technology - EDBT ’13. https://doi.org/10.1145/2452376.2452453
Ore, O., & Sposato, M. (2021). Opportunities and risks of artificial intelligence in recruitment
and selection. International Journal of Organizational Analysis, 30(6).
https://doi.org/10.1108/ijoa-07-2020-2291
Panayides, J. (2023). How is Predictive Analytics Shaping the Future of HR? MyHRfuture.
https://www.myhrfuture.com/blog/how-is-predictive-analytics-shaping-the-future-of-
hr#:~:text=Predictive%20analytics%20can%20also%20help
Roy, P. K., Chowdhary, S. S., & Bhatia, R. (2020). A Machine Learning approach for
automation of Resume Recommendation system. Procedia Computer Science, 167, 2318–
2327. https://doi.org/10.1016/j.procs.2020.03.284
Sari, M. W., Aima, M. H., & Elfiswandi. (2023). The Effect of Creativity and Digital Literacy
on Business Opportunities and Their Impact on Welfare Levels. International Journal of
Professional Business Review, 8(5), e01675–e01675.
https://doi.org/10.26668/businessreview/2023.v8i5.1675
Schleder, G. R., Padilha, A. C. M., Acosta, C. M., Costa, M., & Fazzio, A. (2019). From DFT
to machine learning: recent approaches to materials science–a review. Journal of Physics:
Materials, 2(3), 032001. https://doi.org/10.1088/2515-7639/ab084b
Singh, R., Sharma, P., Foropon, C., & Belal, H. M. (2022). The role of big data and predictive
analytics in the employee retention: a resource-based view. International Journal of Manpower,
ahead-of-print(ahead-of-print). https://doi.org/10.1108/ijm-03-2021-0197
Soni, M., Gomathi, S., & Adhyaru, Y. (2020, July 1). Natural Language Processing for the Job
Portal Enhancement. IEEE Xplore. https://doi.org/10.1109/ICSSS49621.2020.9202046
Suen, H.-Y., & Hung, K.-E. (2023). Building trust in automatic video interviews using various
AI interfaces: Tangibility, immediacy, and transparency. Computers in Human Behavior, 143,
107713. https://doi.org/10.1016/j.chb.2023.107713
Swapna, H. R., & Arpana, D. (2021). Chatbots as a Game Changer in E-recruitment: An
Analysis of Adaptation of Chatbots. Lecture Notes in Networks and Systems, 201, 61–69.
https://doi.org/10.1007/978-981-16-0666-3_7
Tansley, C., Hafermalz, E., & Dery, K. (2016). Talent development gamification in talent
selection assessment centres. European Journal of Training and Development, 40(7), 490–512.
https://doi.org/10.1108/ejtd-03-2016-0017
Tippins, N., Oswald, F., & McPhail, S. M. (2021). Scientific, Legal, and Ethical Concerns
About AI-Based Personnel Selection Tools: A Call to Action. Personnel Assessment and
Decisions, 7(2). https://doi.org/10.25035/pad.2021.02.001
Vedapradha, R., Hariharan, R., & Shivakami, R. (2019). Artificial Intelligence: A
Technological Prototype in Recruitment. Journal of Service Science and Management, 12(03),
382–390. https://doi.org/10.4236/jssm.2019.123026
Intern. Journal of Profess. Bus. Review. | Miami, v. 8 | n. 5 | p. 01-25 | e02089 | 2023.
25
Albassam, W. A. (2023)
The Power of Artificial Intelligence in Recruitment: An Analytical Review of Current AI-Based Recruitment Strategies
Wang, Z., Huang, B., Wang, G., Yi, P., & Jiang, K. (2023). Masked Face Recognition Dataset
and Application. IEEE Transactions on Biometrics, Behavior, and Identity Science, 1–1.
https://doi.org/10.1109/tbiom.2023.3242085
Wei, M., & Zhou, Z. (2022). AI Ethics Issues in Real World: Evidence from AI Incident
Database. ArXiv:2206.07635 [Cs]. https://arxiv.org/abs/2206.07635
Yam, J., & Skorburg, J. A. (2021). From human resources to human rights: Impact assessments
for hiring algorithms. Ethics and Information Technology, 23. https://doi.org/10.1007/s10676-
021-09599-7
Yarger, L., Cobb Payton, F., & Neupane, B. (2019). Algorithmic equity in the hiring of
underrepresented IT job candidates. Online Information Review, 44(2), 383–395.
https://doi.org/10.1108/oir-10-2018-0334
Zel, S., & Kongar, E. (2020). Transforming Digital Employee Experience with Artificial
Intelligence. 2020 IEEE / ITU International Conference on Artificial Intelligence for Good
(AI4G). https://doi.org/10.1109/ai4g50087.2020.9311088
Zimmermann, T., Kotschenreuther, L., & Schmidt, K. (2016). Data-driven HR - R\'esum\'e
Analysis Based on Natural Language Processing and Machine Learning. ArXiv:1606.05611
[Cs], 1(2). https://arxiv.org/abs/1606.05611
