In the era of globalization, businesses navigate a landscape of intensified international competition [1]. Technological advances have fostered increased accessibility of foreign markets for businesses. Although this has expanded market reach, it has also exposed them to heightened levels of competition. Consequently, enterprises are compelled to maintain a competitive advantage to ensure their survival [2]. To maintain competitiveness, businesses must prioritize hiring the right workforce. Acknowledged as the most critical asset within any organizational framework [3], employees possess the knowledge, skills, and expertise essential for an entity’s functioning. Research establishes the correlation between employees’ productivity and organizational performance [4]. Given the pivotal role of employees, organizations are using more stringent hiring practices. The aim is to hire workers with the requisite skills and qualifications to perform effectively [5]. Organizations using strict hiring criteria increase the probability of acquiring high-performing employees. Research stipulates that employees suitably matched to their functions exhibit more productivity and motivation [6]. Hiring unsuitable candidates extends to heightened turnover rates [7], increasing costs and disruptions for businesses [8]. Legislative measures have been established to address workplace discrimination. Notably, Title VII of the Civil Rights Act of 1964 in the United States (US) prohibits intentional exclusion based on identity and the unintentional disadvantage of a protected class through a facially neutral procedure [9]. While identity-based forms of discrimination have become diminished over time, unintentional biases persist [10].

The integration of AI in hiring is not a recent development. In the late 20th century, the initial adoption of AI in hiring focused on automating routine tasks like resume screening and application sorting. The early systems were designed to enhance efficiency by reducing the time and effort invested in such tasks. However, the sophistication inherent in contemporary AI hiring technologies was absent in the early iterations. For instance, resumix, introduced in 1988 and later acquired by HotJobs in 2000, is one of the earliest examples of resume parsing tools [15]. The tool deployed AI to read resumes and extract specific keywords, work experiences, and educational qualifications. Advancements in the 1990s saw the integration of job posting sites like CareerBuild with an application tracking system (ATS) to improve the sourcing of applications [16]. The early 2000s saw the emergence of talent assessment tools like eSkill and SkillSurvey, leveraging AI for automating pre-employment testing and reference checks. The 2010s marked the emergence of AI-powered video interviewing software. For instance, HireVue gained prominence in the mid-2010s for utilizing machine learning (ML) algorithms to assess candidates based on analysis of facial expressions, speech patterns, and body language. In a notable recent example, Amazon attempted to automate its recruitment process using AI in 2018. The algorithm, trained on decades of resumes, aimed to optimize the selection of the most appropriate candidates from a large pool of applicants. However, the tool faced significant challenges and was ultimately abandoned due to allegations of biases [17]. The algorithm, trained on resumes predominantly submitted by male applicants over a ten-year period, exhibited a preference for male-centric language patterns, discriminating against female-oriented applicants [17]. Despite Amazon’s setback, commercially available online hiring tools like Talenture, fetcher, TurboHire, and Findem are widely used.

The availability of data has facilitated the evolution of AI-powered tools, enabling the extraction of new insights through computational analysis [18]. However, the development has given rise to unintended consequences. Algorithmic screening tools that appear evidence-based have emerged as purported alternatives to subjective human evaluations [19]. Divergent opinions exist on the objectivity of algorithmic techniques in mitigating biases. Some scholars assert that algorithmic techniques, especially those utilizing deep learning, are inherently bias-proof, affording businesses an objective way of selecting candidates [20]. 

Algorithmic biases emerge in various forms. Firstly, measurement bias emerges from the identification and measurement of specific features [22]. This type of bias occurs when training data for AI algorithms inadequately represents the intended construct it seeks to measure [23]. In hiring, measurement bias can manifest when training data do not accurately capture the skills and other traits relevant to the job. If an organization continues to hire more white than Black applicants, it may associate good performance with being white given the availability of data about the performance of white employees. Measurement bias can be addressed by auditing and updating training data regularly to ensure they reflect the evolving requirements of a job. The second type, representation bias, results from the ways in which researchers sample populations during data collection, causing non-representative samples that do not represent the entire population [23]. In hiring, this type of bias manifests in under-representation and over-representation of particular demographic groups. 

Moreover, omitted variable bias occurs when hiring algorithms have one or more important variables missing in a model [24], affecting how systems make predictions. Additionally, linking biases occur when biases and attributes from user connections misrepresent the behavior of users [25]. While analyzing users’ connections, hiring algorithms may draw conclusions that do not align with the actual behavior of individuals. For instance, an algorithm over-relying on technical skills without factoring in interpersonal skills may overlook candidates excelling in critical components like communication. The omitted variable bias can be addressed by undertaking a thorough analysis to include all the relevant variables in a model. The last type is aggregation bias, arising from false conclusions made regarding individuals based on an analysis of the entire population [25]. Aggregation biases cause hiring models to ignore individual differences, making them unsuitable where diversity is involved [20].

The CV ranking process, as an initial stage in hiring, involves the assessment of applicants’ CVs by recruiters or HR professionals to identify candidates possessing the required qualifications and experiences for specific positions [28]. This crucial step establishes whether an applicant advances to subsequent stages like interviews and assessments. Recruiters use CV ranking to sift through a large pool of applicants and identify potentially suitable candidates for a job. Given the volume of applications HR professionals receive, specific criteria or qualifications are established to rank CVs [28]. CV ranking is crucial in streamlining the recruitment process, enabling organizations to focus on evaluating the most promising candidates. It optimizes time and resource efficiency in hiring, allowing recruiters swift assessment of applicants’ qualifications and skills, making informed decisions about candidates who should proceed to the next stage. Through CV ranking, HR professionals eliminate unsuitable candidates, saving time. Simultaneously, the CV ranking process influences an applicant’s perception of the hiring organization. From the initial interactions with an organization, applicants form impressions regarding the fairness and transparency of the entire recruitment process. Perception of unfairness in the CV ranking process may dissuade potential candidates from applying, depriving a business of a qualified labor force.

The CV ranking process, while integral to hiring, introduces the risk of bias and discrimination. HR professionals may harbor unconscious biases that influence their CV evaluation. Implicit bias, manifested in unconscious attitudes and stereotypes that people may hold towards particular groups [29], is a major contributor to hiring discrimination. Researchers use the Implicit Association Test (IAT) to measure the unconscious association of specific traits with certain demographic groups [30]. The results confirm that people tend to associate negative traits with racial minorities [31].

Stereotypes influence perceptions and evaluations unconsciously, significantly impacting employment decisions. Studies indicate that when evaluating people from stereotyped groups, individuals tend to concentrate on information aligning with stereotypes and interpret data to affirm the stereotypes [33]. Countering stereotypes poses challenges in the CV ranking process, especially for stigmatized groups expected to conform to established stereotypes [34]. Deviation from stereotypes may result in a good performance being dismissed as mere luck. Consequently, the anticipation of biased treatment adversely impacts the performance of stereotyped groups.

5.2. Non-Technical Solutions in Ranking CVs