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Implications of Utilizing Artificial Intelligence in Assessing and Hiring Employees

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Implications of Utilizing Artificial Intelligence in Assessing and Hiring Employees

Introduction

In a highly competitive market, getting and retaining the best employees require additional analytical tools such as artificial intelligence. Artificial intelligence has often been used to help assess and hire staff without bias or discrimination (Van den Broek et al., 2019). The algorithmic decision-making process, as done by artificial intelligence, helps a company's management to hire the most qualified staff based on their skills. Unlike the previous recruitment processes where a panel of human resources personnel cross-checked the applicant's knowledge, an algorithm is used to ascertain the candidate's knowledge, personality, and required knowledge (Köchling & Wehner, 2020). Therefore, companies that need to recruit the best talents without bias must use artificial intelligence because of its highest rate of impartiality and discrimination. The critical implication of utilizing artificial intelligence in hiring includes efficiency and overcoming bias, which consequently leads to hiring the most qualified.

Literature Review

Van den Broek et al. (2019) argues that the contemporary corporate domain is changing, with numerous applications received for every job opportunity advertised. The process of analyzing the applicant’s data manually may take longer. As the personnel categorizes data while tired, they will likely make mistakes and choose the least qualified. Research by Köchling & Wehner (2020) inferred that the application of artificial intelligence helps in sorting the extensive data on time and helps them get the most qualified candidate. As technology advances, it becomes impossible for manual applications to be accurately analyzed. Instead, the online application requires artificial intelligence to seek out the candidates and shortlist the ones who are qualified for the job and have the necessary qualifications. Further research inferred that the artificial intelligence and application of algorithms in the hiring process not only speeded up the process but also enhanced quality in the capability.

Getting rid of irrelevant Applications.

Different research have been conducted to determine the benefits of AI in the recruitment process. According to research by Köchling & Wehner (2020), the hiring process has improved accuracy as the algorithms eliminate all irrelevant applications, increasing the process's efficiency. On the contrary, Broek et al., (2019) insinuated that the bulk applications in the contemporary domain encourage companies to have a pool to choose from the best clients. The manual application typically allows people who need the qualification to submit their papers. Consequently, the process becomes cumbersome, jeopardizing quality (Brishti & Javed, 2020). The introduction of artificial intelligence introduces algorithms that filter the applications based on relevance and qualifications.

The filtering process helps to ensure that only the relevant applicants pass the final stage of the selection process from the pool of applicants who are interested in the position. Brishti & Javed’s research in 2020 shared similar sentiments with Köchling & Wehner (2020) that the new technology helps in sampling the applications, and only qualified candidates make it to the shortlisting. Therefore, the application of the technology has led to a better recruitment process in due course.

Improving the Objectivity of the Recruitment

The recruitment process becomes objectives when the required aspects are all fulfilled. In some positions, numerous requirements are needed to ensure a candidate has the right hard and soft skills. Artificial intelligence and related algorithms help to determine whether a candidate has the correct variety of the required soft and hard skills (Köchling & Wehner, 2020). As people show the right combination of the necessary hard and soft skills, it becomes possible to meet employment standards. Similarly, Brishti & Javed (2020) inferred that objectivity could only be achieved when a relevant algorithm is used.

In the contemporary domain, it is common for some candidates to be met with discrimination because of race, culture, or religion, as artificial intelligence uses an embedded system to filter the candidates and looks for qualifications in every candidate without considering the other factors that may result in discrimination (Broek et al., 2019). Since discrimination deprives companies of good employees, artificial intelligence enables companies to perform better and have applicants selected based on their knowledge and experience, not on social factors that may disadvantage some candidates. However, it is imperative to note that research by Brishti & Javed (2020) proved that some pre-programmed algorithms might lead to selected discriminations in the long run. It is, therefore, the role of human resource management to use the algorithm to ensure that it does not have any inbuilt bias.

Artificial Intelligence Lacks Human Touch and May Select A Person without Humanity

Some aspects of the hiring process require a person's humanity to be involved in making the decisions, such as rational thinking and making informed decisions. For example, artificial intelligence does not understand the candidate’s moral values, beliefs in the family systems, and moral values (Broek et al., 2019). Consequently, it implies that organizations can be used to achieve family values. Therefore, it is not entirely reliable in the hiring process to ensure that the people may not get the required company values. Some organizations deeply believe in family values because AI cannot offer family values as necessary for a better outcome.

Analysis of literature

In the contemporary corporate domain, a holistic hiring process is required to obtain all-rounded candidates equipped with soft and hard skills. Although AI is essential in introducing amplitude tests that help determine the personality of candidates, the most profound moral values and beliefs may still need to be achieved or overcome in the discourse (Brishti & Javed, 2020). It is imperative to note that even though artificial intelligence increases speed and offers subjectivity to the process, it lacks the human touch and therefore makes the entire process prone to errors and implied biases as the people who program the algorithms may complete it to favor a particular set of values.

Human resource departments intending to have a holistic and speedy recruitment process must consider using AI. The technology used in the hiring process makes it fast, eliminates all forms of bias, and instils objectivity. However, negative implications come from usage, such as lacking a human touch in the design van den (Broek et al., 2019). It is, therefore, the role of human resource management to ensure that the algorithm it uses is customized to meet the organization's unique needs and eliminate the bias and negative implications of the algorithms in the hiring process.

References

Brishti, J. K., & Javed, A. (2020). The viability of AI-based recruitment process: A systematic literature review, (dissertation). https://www.diva-portal.org/smash/get/diva2:1442986/FULLTEXT01.pdf

Köchling, A., & Wehner, M. C. (2020). Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development.  Business Research,  13(3), 795-848. https://link.springer.com/article/10.1007/s40685-020-00134-w

van den Broek, E., Sergeeva, A., & Huysman, M. (2019). Hiring algorithms: An ethnography of fairness in practice. Association for Information Systems https://core.ac.uk/download/pdf/301385085.pdf

Implications of using AI in Employee Recruitment

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Implications of using AI in Employee Recruitment

Discussion

Organizations are increasingly using artificial intelligence-based algorithms in the process of recruiting employees (Jognson et al., 2020). This is the hiring tool of the future. The AI systems are aiding the access to the workforce in the market, but they are also leading to the introduction of biases and perpetuation of disparities in hiring workers. Many major employers apply AI and automation in assessing and recruiting employees (Jognson et al., 2020). AI can measure personality, but it lacks reliability as it can integrate or prevent the hiring of certain workers without full knowledge of the applicants' capabilities. There has been substantial evidence that AI leads to discrimination which results in inequalities in employment opportunities. Discrimination is generally based on aspects such as gender, race and ethnicity. There is an urgent need for organizations to figure out how to prevent discrimination when using AI in hiring, as the more entities rely on the algorithms, the more potential employees will get discriminated against.

Köchling & Wehner (2020) provided recommended that an improved algorithmic decision-making tool be used for the recruitment and hiring process. This tool should be combined with human resource skills for good governance to ensure the computer-generated decision have some human touch. The recommendation by Javed & Brishti (2020) was that more research is necessary to improve the functioning of AI in recruiting workers, as the downsides of the tool are significant. They, however, also recommended that hiring managers can benefit from using this technology. Van den Broek et al. (2019) and Wright & Atkinson (2019) stated that entities should not place substantial confidence in using AI to accomplish their recruitment objectives.

From the review of the articles, there is more progress and developments required before AI-based algorithms are fully implemented in recruiting workers. According to all articles involved, from Javed & Brishti (2020 to Köchling & Wehner (2020), more research should help ensure that biases and discrimination in hiring are eliminated. This will mean that minority populations such as women and African Americans are not disadvantaged when applying for jobs that they are qualified for. The second recommendation is that AI can be applied, as despite its disadvantages, its application has immense benefits. AI should help hiring managers improve the quality of their hires, help in the automation of tedious manual tasks and improve the candidate experience. AI should also help minimize the hiring time and make the process more cost-effective (Jognson et al., 2020). The application should come with cautiousness in order to avoid any drawbacks.

References

Brishti, J. K., & Javed, A. (2020). The viability of ai-based recruitment process: A systematic literature review. https://www.diva-portal.org/smash/get/diva2:1442986/FULLTEXT01.pdf

Elmira van den Broek, E., Sergeeva, A., & Huysman, M. (2019). Hiring algorithms: An ethnography of fairness in practice. https://core.ac.uk/download/pdf/301385085.pdf

Jognson, R. D., Stone, D. L., & Lukaszewski, K. M. (2020). The benefits of eHRM and AI for talent acquisition. Journal of Tourism Futures, 7(1), 40-52. https://doi.org/10.1108/JTF-02-2020-0013

Köchling, A Wehner, M. C. (2020). Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development.  Business Research, 13(3), 795-848. https://doi.org/10.1007/s40685-020-00134-w

Wright, J., & Atkinson, D. (2019). The impact of artificial intelligence within the recruitment industry: Defining a new way of recruiting.  Carmichael Fisher, 1-39. https://www.cfsearch.com/wp-content/uploads/2019/10/James-Wright-The-impact-of-artificial-intelligence-within-the-recruitment-industry-Defining-a-new-way-of-recruiting.pdf

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Literature Review: Cybersecurity Readiness in SMEs

Introduction to Literature Review

Small and medium enterprises (SMEs) encounter risks to cybersecurity safety as they implement technological processes in their business operations (Perozzo et al., 2021). Following the cybersecurity risks that SMEs face in the ever-growing business world, there is need for research to help determine the level of preparedness and offer possible remedies for the cybersecurity safety issue. The literature review comprised only research articles and literature about the status of readiness of SMEs when it comes to cybersecurity challenges. The factors that were reviewed in the study were the organizational readiness of SMEs, different cyber threats to SMEs and their impacts, and the possible preventive measures SMEs can use against cyberattacks. The literature review process was conducted in Monroe College Library databases like ProQuest, EBSCO Host, and Google Scholar. SMEs, cybersecurity organizational preparation, and cybersecurity were some of the search phrases used to locate pertinent publications for the literature review.

Review of Literature

Cybersecurity Awareness

Perozzo et al. (2021) conducted a qualitative multiple-case study in Italy to explore existing literature on cybersecurity. This study was published in the Complex Systems Informatics and Modeling Quarterly (CSIMQ) journal as article 185 and issue number 33, from pages 53 to 66. The paper follows a multiple case study using multiple resources to explore real-life cases over time. The authors aimed to explore the limitations of the readiness models of small and medium enterprises (SMEs) for cybersecurity and proposed a framework that may be used. The authors use a qualitative multiple-case study method to understand the socio-cultural context of the study participants. Perozzo et al. (2021) collected data through semi-structured interviews and