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The purpose of this paper is to offer a critical analysis of talent acquisition software and its potential for fostering equity in the hiring process for underrepresented IT professionals. The under-representation of women, African-American and Latinx professionals in the IT workforce is a longstanding issue that contributes to and is impacted by algorithmic bias.

Sources of algorithmic bias in talent acquisition software are presented. Feminist design thinking is presented as a theoretical lens for mitigating algorithmic bias.

Data are just one tool for recruiters to use; human expertise is still necessary. Even well-intentioned algorithms are not neutral and should be audited for morally and legally unacceptable decisions. Feminist design thinking provides a theoretical framework for considering equity in the hiring decisions made by talent acquisition systems and their users.

This research implies that algorithms may serve to codify deep-seated biases, making IT work environments just as homogeneous as they are currently. If bias exists in talent acquisition software, the potential for propagating inequity and harm is far more significant and widespread due to the homogeneity of the specialists creating artificial intelligence (AI) systems.

This work uses equity as a central concept for considering algorithmic bias in talent acquisition. Feminist design thinking provides a framework for fostering a richer understanding of what fairness means and evaluating how AI software might impact marginalized populations.

A candidate’s cultural fit is often just as important as their skill set, but more difficult to screen for. Behavioral interviewing, which involves probing into a candidate’s past behavior to predict their future behavior and performance, gives recruiters a reliable indication of whether or not a candidate will succeed in a specific role and carries the significant added benefit of helping minimize the influence of unconscious bias.

The authors explore three reasons why behavioral interviewing is more relevant now than ever before. They also outline specific recommendations for how companies can implement behavioral interviewing.

While it is hard to find enough hours in the day without adding the burden of interviewer training to your plate, there is never been a better time to embrace behavioral interviewing. Failure to train hiring managers and interviewers how to interview effectively can result in a poor candidate experience, and an over-reliance on alternatives such as skills-fit and culture-fit interviewing techniques will prove far less effective in determining the best candidate for the job.

This article explores three key topics among HR practitioners – hiring, culture and retention – and helps to identify how companies can alter their hiring/interviewing process to achieve a better candidate fit for their organization.

Rachel Banning was assigned a new role in HR with the responsibility to update recruitment and orientation systems to meet the rapidly growing demand for manpower at McCune Contracting, an oilfield services provider in Alberta, Canada. McCune’s industry peers were competing to attract the same skilled employees, within a relatively small talent pool. The HR team was only a few short weeks away from the upcoming peak “turnaround season” when they would be expected to recruit and deploy 500 new temporary workers for their clients’ sites. Banning knew she had to take immediate actions to fix as many of the systems issues as possible and to eventually set the team up with a more permanent solution for systems integration.

The authors had access to McCune Contracting to complete field research for this case.

The case is designed for business students at both graduate and undergraduate levels. The case can be used in operations management courses to discuss the topic of process analysis and operations strategy or in management information system courses as a comprehensive case study for use at the end of the course. The case might particularly appeal to students who have worked in human resources management areas or the service industry.

Theoretical underpinnings include a process view of organizational performance, internal supplier and internal customer orientation, performance improvement, information systems integration and value chain analysis.

The Internet and social media have fundamentally transformed the ways in which individuals find jobs. Relatively little is known about how demand-side market actors use online information and the implications for social stratification and mobility. This study provides an in-depth exploration of the online recruitment strategies pursued by human resource (HR) professionals. Qualitative interviews with 61 HR recruiters in two southern US metro areas reveal two distinct patterns in how they use Internet resources to fill jobs. For low and general skill work, they post advertisements to online job boards (e.g., Monster and CareerBuilder) with massive audiences of job seekers. By contrast, for high-skill or supervisory positions, they use LinkedIn to target passive candidates – employed individuals who are not looking for work but might be willing to change jobs. Although there are some intermediate practices, the overall picture is one of an increasingly bifurcated “winner-take-all” labor market in which recruiters focus their efforts on poaching specialized superstar talent (“purple squirrels”) from the ranks of the currently employed, while active job seekers are relegated to the hyper-competitive and impersonal “black hole” of the online job boards.

Purpose: In recent times, ‘artificial intelligence (AI)’ has been pervasive even in organisations or at home. AI is defined as programming computers or other technological devices to act, react, respond, or assist the same way humans do. AI has undeniably made people’s lives easier. In organisations, the impact of AI is even more visible. The main aim of this chapter is to examine the significant role of future work skill’s (FWS) each component in the field of on-growing automation. The focus will be especially on emotional and social intelligence (ESI) (a key component of FWS) while adopting AI.

Need of the Study: In terms of human resource management (HRM), AI is useful for people management, payroll services, staff monitoring and improving the recruiting network, among other things. Even managers put their organisation’s job openings on the web and get applicant resumes electronically. People and employees in the organisation have become more advanced and innovative due to AI. A device obtains employee attendance, and human resource (HR) can track their employees and their organisation’s workforce data. HR has now been awarded more authority to manage and fix their employee’s problems because of AI. In a rapidly changing world, AI is affecting all aspects. AI is yearning to automate all of the jobs.

Methodology: Now a question arises how we can stay relevant in AI economic development? As humans, we learned that every issue is a problem of optimisation because we simply require human skills to develop, create and innovate new things. Therefore, researchers recognised that adopting sustainable growth skills encourages people to continue learning throughout their lives. Moreover, AI has enabled machines the ability to learn over time. Still, they will never be able to develop new ideas like human intelligence. A machine can use only one fixed data algorithm. Now humans have made significant progress in various fields with the help of FWS; without integrated computer sciences, brain science would not make such an outstanding achievement. On the other hand, human minds are masters of their intelligence, such as creativity, complex problem-solving, cognitive thinking, ESI and communication. Breakthrough human mind are masters of algorithms represented people have to understand new trends of technology around us, and the best way to move forward is to be aware, adapt and update skills.

Practical Implications: However, AI is required because, regardless of technological advancements, AI is leading Industry 4.0. The industry’s transformation is in 4.0, and hopefully, 5.0 will jump on board soon. Undoubtedly, AI should streamline the process and eliminate redundancy or administrative tasks.

Finding: AI can be more effective in organisations if they incorporate other FWS, particularly the soft human ESI skills, whereas AI is present everywhere, we can still not neglect FWS, especially ESI. So, this chapter highlights the important role of soft skills, that is, ESI and FWS, while adapting AI for an effective HRM.

Functionality of generating human capital of educated workers and citizens is core to higher education. This chapter explores the long-term relationship for the academy to support the needs for advanced degree and certificate programs, executive education, career preparation, and lifelong service opportunities focused on companies and their needs.

The purpose of this study is to contribute to the knowledge on the opportunities and risks in the use of artificial intelligence (AI) in recruitment and selection by exploring the perspectives of recruitment professionals in a multicultural multinational organisation.

A qualitative approach was used in this exploratory study. Face-to-face, semi-structured in-depth interviews were conducted with ten professional recruiters who worked for a multinational corporation.

The findings revealed that AI facilitates the effective performance of routine tasks through automation. However, the adoption of AI technology in recruitment and selection is also fraught with risks that engender fear and distrust among recruiters. The effective adoption of AI can improve recruitment strategies. However, cynicism exists because of the fears of job losses to automation, even though the participants thought that their jobs would continue to exist because recruiters should always be humans.

This paper provides a unique exploration of the opportunities and risks in the adoption of AI for the recruitment and selection function in human resource management. The benefits are the delegation of routine tasks to AI and the confirmation of the crucial role of professional recruiters.

The number of candidates applying to public contests (PC) is increasing compared to the number of human resources employees required for selecting them for the Police Force (PF). This work intends to perceive how those public institutions can evaluate and select their candidates efficiently during the different phases of the recruitment process. To achieve this purpose, artificial intelligence (AI) was studied. This paper aims to focus on analysing the AI technologies most used and appropriate to the PF as a complementary recruitment strategy of the National Criminal Investigation police agency of Portugal – Polícia Judiciária.

Using design science research as a methodological approach, the authors suggest a theoretical framework in pair with the segmentation of the candidates and comprehend the most important facts facing public institutions regarding the usage of AI technologies to make decisions about evaluating and selecting candidates. Following the preferred reporting items for systematic reviews and meta-analyses methodology guidelines, a systematic literature review and meta-analyses method was adopted to identify how the usage and exploitation of transparent AI positively impact the recruitment process of a public institution, resulting in an analysis of 34 papers between 2017 and 2021.

Results suggest that the conceptual pairing of evaluation and selection problems of candidates who apply to PC with applicable AI technology such as K-means, hierarchical clustering, artificial neural network and convolutional neural network algorithms can support the recruitment process and could help reduce the workload in the entire process while maintaining the standard of responsibility. The combination of AI and human decision-making is a fair, objective and unbiased process emphasising a decision-making process free of nepotism and favouritism when carefully developed. Innovative and modern as a category, group the statements that emphasise the innovative and contemporary nature of the process.

There are two main limitations in this study that should be considered. Firstly, the difficulty regarding the timetable, privacy and legal issues associated with public institutions. Secondly, a small group of experts served as the validation group for the new framework. Individual semi-structured interviews were conducted to alleviate this constraint. They provide additional insights into an interviewee’s opinions and beliefs.

Ensure that the system is fair, transparent and facilitates their application process.

The main contribution is the AI-based theoretical framework, applicable within the analysis of literature papers, focusing on the problem of how the institutions can gain insights about their candidates while profiling them, how to obtain more accurate information from the interview phase and how to reach a more rigorous assessment of their emotional intelligence providing a better alignment of moral values. This work aims to improve the decision-making process of a PF institution recruiter by turning it into a more automated and evidence-based decision when recruiting an adequate candidate for the job vacancy.

Andy Leftley, recruitment manager at DML, discusses the company’s successful first step into e‐recruitment and the significant benefits that have been realised from its hiring management system.