THE ROLE OF ARTIFICIAL INTELLIGENCE IN SHAPING CURRENT AND FUTURE PRACTICES IN HUMAN RESOURCES RECRUITMENT AND SELECTION

ABSTRACT

Artificial Intelligence (AI) broadly refers to a variety of computational methods capable of imitating human decision-making and operational behaviors with such sophistication that they are often perceived as intelligent, particularly in their ability to rapidly process vast datasets. Given its transformative potential, AI is increasingly being adopted across organizational domains, notably within human resources (HR) management, where it plays a growing role in recruitment and selection processes. Technologies such as big data algorithms have significantly extended the reach and efficiency of candidate identification. Yet, despite these advances, important concerns remain, particularly regarding ethical implications and the perceptions of those engaging with AI-driven systems—recruiters, hiring managers, and candidates alike. These unresolved issues underscore the necessity for more rigorous empirical research and systematic reviews. Within this framework, the present study critically examines the application of AI in HR recruitment and selection, highlighting prevailing challenges and forecasting future developments based on existing scholarly literature.

АННОТАЦИЯ

Искусственный интеллект (ИИ) охватывает вычислительные методы, имитирующие человеческое принятие решений и быстро обрабатывающие большие объемы данных. Благодаря своему потенциалу ИИ активно внедряется в управление персоналом, особенно в процессы найма и отбора. Алгоритмы больших данных повысили эффективность поиска кандидатов, однако остаются этические вопросы и проблемы восприятия со стороны рекрутеров и соискателей. Такие технологии, как алгоритмы больших данных, существенно расширили охват и повысили эффективность идентификации кандидатов. Тем не менее, несмотря на эти достижения, остаются важные вопросы, в частности, связанные с этическими аспектами и восприятием систем, управляемых ИИ, со стороны рекрутеров, менеджеров по найму и самих соискателей. Настоящее исследование анализирует применение ИИ в HR, выявляет текущие вызовы и прогнозирует будущие направления на основе научной литературы.

Keywords: artificial intelligence, human resource management, recruitment strategies, selection practices, technological applications, ethical considerations.

Ключевые слова: искусственный интеллект, управление человеческими ресурсами, стратегии найма, практики отбора, технологические приложения, этические аспекты.

Introduction

The landscape of employment in the 21st century has become inseparably intertwined with the integration of information technologies and their diverse applications, marking what many describe as the advent of a fourth industrial revolution [1]. The concept of the fourth industrial revolution, introduced by Klaus Schwab [2], founder and executive chairman of the World Economic Forum, captures the rapid evolution of the digital era that has been unfolding since the mid-20th century. This transformation is distinguished by the convergence of physical, digital, and biological systems [3]. Within the broad array of technological innovations associated with this revolution—such as 3D printing, quantum computing, nanotechnology, biotechnology, and alternative energy technologies [4]—artificial intelligence (AI) has emerged as a particularly significant and disruptive force, drawing widespread attention across the media, academia, and industry.

First defined by John McCarthy in 1955, AI encompasses a wide spectrum of computational techniques capable of replicating human decision-making and cognitive processes with such fidelity that they appear intelligent, notably through the ability to rapidly analyze large volumes of data to identify, correlate, and predict patterns [5]. Recognized for its transformative potential, AI is being increasingly adopted—or seriously considered—for deployment across a range of organizational functions. A global survey of executives indicates that 85% intend to make substantial investments in AI technologies within the next three years [6]. Predictions further suggest that AI will profoundly reshape the business environment throughout the 21st century [7]. Numerous white papers and scholarly reports emphasize the advantages of AI implementation, proposing that it holds the power to revolutionize not only organizations and industries but society at large [8]. Overall, AI can be classified as a disruptive technology, poised to fundamentally redefine the way we live and work [9].

Despite the rapid advancements in AI capabilities, there remains a limited and fragmented understanding of its practical applications, impacts, and critical success factors within organizational contexts [10]. This gap is particularly evident in the domain of human resources management (HRM), and even more specifically in HR recruitment and selection practices. More than half of the organizations currently utilizing AI report its use primarily to enhance and streamline hiring processes [11,12]. AI facilitates substantial cost reductions, particularly by minimizing time expenditure, recruiter effort, and the redundancy of administrative tasks. It also benefits candidates by shortening organizational response times, thus contributing to a stronger employer brand [13]. Nevertheless, the deployment of AI in recruitment and selection processes is not without controversy. Critical concerns have been raised about its potential adverse effects, including negative implications for organizational diversity management [14] and the risk of human labor displacement in certain operational areas [15]. Furthermore, important questions persist regarding the ethical dimensions of AI use, as well as the perceptions and attitudes of its principal stakeholders—recruiters, hiring managers, and candidates [1]. These unresolved issues underscore the urgent need for more comprehensive empirical research and systematic literature reviews.

Within this context, the present study explores the applications of AI in HR recruitment and selection, analyzing current challenges and future trends as outlined in the existing body of scholarly literature.

AI Applications in HR Recruitment and Selection Processes

In general, artificial intelligence (AI) refers to a broad range of computational techniques intended to accurately mimic human behavioral and decision-making processes, which are thought to be intelligent due to their ability to quickly process enormous amounts of data and recognize, relate, and forecast patterns [5].  Accordingly, AI is also defined as having the capacity to make decisions in real time using pre-programmed algorithms and computational models created via data analysis, allowing systems to independently adjust and offer ever-more-definable responses to changing circumstances [16] (p. 1).  These skills demonstrate its great promise for tasks essential to HR recruitment and selection, especially those involving decision-making and data processing (e.g., sourcing and filtering) [17, 18]. As a result, organizations are progressively integrating AI into their HR functions.

AI applications fall into five main categories, according to Wisskirchen et al. [5]: (1) machine learning (ML), which is the creation of programs that improve performance by exposing them to data or prior experiences [19]; (2) robotics, which is the use of machines that can perform tasks autonomously and mimic human behavior; (3) dematerialization, which is the conversion of physical products into digital forms; (4) the gig economy, which is defined by work platforms that enable team-based or on-demand task completion; and (5) autonomous driving, in which vehicles navigate on their own using sensor technologies. This topic focuses on ML and robotics since they are especially relevant to HR recruitment and selection procedures.

Despite being frequently linked to deep learning (DL), a more complex subset of machine learning, machine learning has a different fundamental methodology. DL uses artificial neural networks, which allow machines to learn from data on their own without explicit programming, even though both approaches use algorithms to model abstract data representations [19]. Computers can now carry out complicated tasks like finding patterns in large datasets and making predictions on their own thanks to machine learning algorithms. Natural language processing (NLP) technologies, for example, make it possible to quickly and automatically gather and analyze a variety of data sources [20]. While frequently conflated with data mining, ML and DL distinguish themselves by not only exploring and describing data but also predicting outcomes through the recognition of underlying patterns. Depending on the specific objectives, various algorithm types—such as supervised, unsupervised, semi-supervised, and reinforcement learning—may be employed [21]. In HR recruitment and selection, ML and DL are applied to critical areas including anomaly detection, background checks, content personalization, and managing ethical and data considerations involving images, video, and speech recognition, as outlined by Rogers et al. [16]. Robotics, broadly defined, encompasses the use of robotic systems capable of executing automated tasks and, depending on their programming and incorporation of ML/DL elements [22], even simulating human behavior. It is useful to distinguish among different robotic tools: bots (automated programs executing repetitive tasks online), co-bots (collaborative robots designed to work alongside humans), and chatbots (interactive bots created to engage in dialogue and provide predefined responses). Within HR recruitment and selection, bots and chatbots are particularly relevant. Bots can efficiently identify suitable candidate profiles, thereby accelerating pre-selection processes. Chatbots, on the other hand, facilitate real-time interactions with applicants, conducting initial screening interviews and significantly reducing organizational response times, thus enhancing the candidate experience [23]. Recent examples of such implementations are given by Fraij and László [24], who cite chatbot systems used by large companies like Ikea and Amazon, such as Xor (https://xor.ai/ (accessed on 1 August 2023)) and Talkpush (http://www.talkpush.com/ (accessed on 1 August 2023)). All things considered, artificial intelligence (AI) offers a collection of tools and technologies that can assist and streamline a wide range of HR recruitment and selection procedures [25]. An overview of the primary AI applications in various fields is given in Table 1.

Table 1.

Overview of the primary AI applications in various fields

AI Trends and Challenges in Recruitment and Selection Processes

There are many benefits to incorporating AI into HR's hiring and selection procedures. Some of the most noteworthy are considerable decreases in time, money, effort, and human capital expenses [13, 26]. AI allows HR professionals to focus on higher-order duties like critical thinking, oversight, decision-making, and meaningful interaction with candidates by automating repetitive chores. Due to its ability to handle enormous datasets, AI also makes it easier to access a wide range of data sources, such as vast repositories of resumes and social media posts, which were previously challenging and time-consuming to process manually [26]. Along with increasing the predicted accuracy of recruiting outcomes [26], this technology capability also makes it easier to promote organizational diversity [28] and talent acquisition [27]. On the one hand, by effectively identifying qualified applicants, AI applications like machine learning (ML) can maximize the first stages of hiring. However, AI has the potential to lessen biases in selection processes, especially those related to age, gender, and other demographic characteristics.

However, the very characteristics that make AI so effective also present serious hazards and difficulties, which need to be taken into consideration as new developments in HR procedures emerge. The limitations of AI, including applications like machine learning, can be divided into four main categories, as noted by González et al. [26]: problems with data quality, opacity in "black box" predictive systems, ethical and legal issues, and how users (such as recruiters, selection managers, and applicants) react to AI-based interventions. Reliance on AI for decision support, for example, may reduce the autonomy of HR professionals by making them believe that selection results are preset and decreasing their accountability for recruiting decisions [29]. Moreover, AI systems can inadvertently reinforce biases present in their training data, thus perpetuating discriminatory patterns. Unconscious biases embedded within the algorithms by AI designers may further exacerbate such outcomes, particularly in the context of recruitment and selection [14].

Initiatives like the one put forth by Rodgers et al. [16] provide useful tactics to mitigate these hazards. In order to encourage the responsible use of AI in HR practices, their methodology places a strong emphasis on creating diverse, multi-stakeholder advisory boards, encouraging tolerance for opposing viewpoints, and integrating ethical monitoring into the creation of AI algorithms.

Looking ahead, collaboration between humans and automated systems will become increasingly essential within recruitment and selection processes. Thus, there is a growing need for broader analytical frameworks that address how AI-driven changes can be systematically integrated to benefit both organizations and individual candidates. Advancing in this direction will require not only empirical research but also stronger interdisciplinary collaboration among HR practitioners, AI developers, computer scientists, legal experts, and professionals from other critical fields involved in the design, implementation, and evaluation of AI technologies within organizational settings.

Conclusions

In addition to examining future trends and the main issues noted in the literature, this study looked at the function of artificial intelligence (AI) and its applications in human resources (HR) recruiting and selection procedures. The use of AI-driven solutions for HR tasks, including as chatbots, bots, natural language processing (NLP), deep learning (DL), and machine learning (ML), has advanced significantly in recent years. The adoption of these technologies is anticipated to continue growing across organizational contexts due to their substantial benefits, which include decreased costs connected with time, effort, financial resources, and staff [13,26]. However, the very qualities that make AI appealing also come with significant risks and drawbacks, which are becoming more widely acknowledged as important issues for the future. These challenges encompass ethical and legal considerations, concerns around diversity, equity, and inclusion (DE&I), and the varied responses of users toward AI integration.

Therefore, it is crucial to work for advancement by encouraging greater multidisciplinary collaboration in addition to empirical study. In order to guide the responsible development, implementation, and evaluation of AI applications within organizational environments, it will be essential to strengthen collaboration among HR practitioners, AI developers, computer scientists, legal experts, and other pertinent professions.

• Support: No outside funding was obtained for this study.
• Statement from the Institutional Review Board: Not relevant.
• Statement of Informed Consent: Not relevant.
• Resource Availability Statement: This article does not permit data sharing.
• Concerns of Bias: No conflicts of interest are disclosed by the author.

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