Ethics Guidelines for Using AI-based Algorithms in Recruiting: Learnings from a Systematic Literature Review

To reduce the workload of employees working in Human Resource departments and to avoid bias in pre-selection of applicants, an increasing number of companies deploy Artificial Intelligence (AI)-based algorithms. Some examples such as Amazon’s discriminating recruiting algorithm showed that algorithms are not free of unethical decision making. Although there already exists a variety of ethics principles for AI-based systems, those are usually hardly being applicable to specific use cases such as using AI-based algorithms in recruiting processes. To address this issue and to provide guidance for researchers and practitioners, we conducted a systematic literature review (keyword and backwards search) on existing ethics guidelines and principles for AI and extracted aspects that seemed applicable to guide recruiting processed. Based on 28 relevant papers we derived actionable guidelines for using AI-based algorithms in recruiting processes. We categorized our guidelines into the aspects of fairness, avoidance of discrimination and avoidance of bias

Fairness as a Determinant of AI Adoption in Recruiting: An Interview-based Study

Traditional recruiting techniques are often characterized by discrimination as human recruiters make biased decisions. To increase fairness in human resource management (HRM), organizations are increasingly adopting AI-based methods. Especially recruiting processes are restructured in order to find promising talents for vacant job positions. However, use of AI in recruiting is a two-edged sword as the neutrality of AI-based decisions highly depends on the quality of the underlying data. In this research-in-progress, we develop a research model explaining AI adoption in recruiting by defining and considering fairness as a determinant. Based on 21 semi-structured interviews we identified dimensions of perceived fairness (diversity, ethics, discrimination and bias, explainable AI) thereby affecting AI adoption. The proposed model addresses research gaps in AI recruiting research in general and arising ethical questions concerning the use of AI in people management in general and recruiting process in particular. We also discuss implications for further research and next steps of this research in progress work

Artificial intelligence applied to potential assessment and talent identification in an organisational context

França, T. J. F., São Mamede, J. H. P., Barroso, J. M. P., & Santos, V. M. P. D. D. (2023). Artificial intelligence applied to potential assessment and talent identification in an organisational context. Heliyon, 9(4), 1-25. [e14694]. https://doi.org/10.1016/j.heliyon.2023.e14694Our study provides valuable insights into the relationship between artificial intelligence (AI) and Human Resource Management (HRM). We have minimised bias and ensured reliable findings by employing a systematic literature review and the PRISMA statement. Our comprehensive synthesis of the studies included in this research, along with a bibliometric analysis of articles, journals, indexes, authors' affiliations, citations, keyword co-occurrences, and co-authorship analysis, has produced robust results. The discussion of our findings focuses on critical areas of interest, such as AI and Talent, AI Bias, Ethics and Law, and their impact on Human Resource (HR) management. Our research highlights the recognition by organisations of the importance of talent management in achieving a competitive advantage as higher-level skills become increasingly necessary. Although some HR managers have adopted AI technology for talent acquisition, our study reveals that there is still room for improvement. Our study is in line with previous research that acknowledges the potential for AI to revolutionise HR management and the future of work. Our findings emphasise the need for HR managers to be proactive in embracing technology and bridging the technological, human, societal, and governmental gaps. Our study contributes to the growing body of AI and HR management knowledge, providing essential insights and recommendations for future research. The importance of our study lies in its focus on the role of HR in promoting the benefits of AI-based applications, thereby creating a larger body of knowledge from an organisational perspective.publishersversionpublishe

The Ethics of People Analytics:Risks, Opportunities and Recommendations

Purpose: This research analyzed the existing academic and grey literature concerning the technologies and practices of people analytics (PA), to understand how ethical considerations are being discussed by researchers, industry experts and practitioners, and to identify gaps, priorities and recommendations for ethical practice. Design/methodology/approach: An iterative “scoping review” method was used to capture and synthesize relevant academic and grey literature. This is suited to emerging areas of innovation where formal research lags behind evidence from professional or technical sources. Findings: Although the grey literature contains a growing stream of publications aimed at helping PA practitioners to “be ethical,” overall, research on ethical issues in PA is still at an early stage. Optimistic and technocentric perspectives dominate the PA discourse, although key themes seen in the wider literature on digital/data ethics are also evident. Risks and recommendations for PA projects concerned transparency and diverse stakeholder inclusion, respecting privacy rights, fair and proportionate use of data, fostering a systemic culture of ethical practice, delivering benefits for employees, including ethical outcomes in business models, ensuring legal compliance and using ethical charters. Research limitations/implications: This research adds to current debates over the future of work and employment in a digitized, algorithm-driven society. Practical implications The research provides an accessible summary of the risks, opportunities, trade-offs and regulatory issues for PA, as well as a framework for integrating ethical strategies and practices. Originality/value: By using a scoping methodology to surface and analyze diverse literatures, this study fills a gap in existing knowledge on ethical aspects of PA. The findings can inform future academic research, organizations using or considering PA products, professional associations developing relevant guidelines and policymakers adapting regulations. It is also timely, given the increase in digital monitoring of employees working from home during the Covid-19 pandemic

The Impact of Signaling Commitment to Ethical AI on Organizational Attractiveness

As organizations drive the development and deployment of Artificial Intelligence (AI)-based technologies, their commitment to ethical and humanistic values is critical to minimizing potential risks. Here, we investigate talent attraction as an economic incentive for organizations to commit to ethical AI. Based on Corporate Social Responsibility (CSR) literature and signaling theory, we present a mixed-methods research design to investigate the effect of ethical AI commitment on organizational attractiveness. Specifically, we i) identify signals of ethical AI commitment based on a review of corporate websites and expert interviews and ii) examine the effect of selected signals on organizational attractiveness in an online experiment. This short paper presents first results on ethical AI signals and details the next steps. Our research will contribute to the theoretical conceptualization of ethical AI as a part of CSR and support managers of digital transformation processes when weighing investments in ethical AI initiatives

Employees as Regulators: The New Private Ordering in High Technology Companies

There is mounting public concern over the influence that high technology companies have in our society. In the past, these companies were lauded for their innovations, but now as one scandal after another has plagued them, from being a conduit in influencing elections (think Cambridge Analytica) to the development of weaponized artificial intelligence, to their own moment of reckoning with the #MeToo movement, these same companies are under scrutiny. Leaders in high technology companies created their own sets of norms through private ordering. Their work was largely unfettered by regulators, with the exception of the Securities and Exchange Commission’s oversight of public companies. Now, however, white-collar employees at high technology companies are speaking out in protest about their respective employers’ actions and changing private ordering as we know it. In essence, employees are holding companies accountable for the choices they make, whether it is what area to work (or not work) in or eliminating a practice that has systemic implications, such as mandatory arbitration provisions for sexual misconduct cases. This Article builds upon my prior work on the role of corporations and social movements, analyzing how employees in high technology companies have redefined the contours of private ordering and, in the process, have also reimagined what collective action looks like. Because these workers are in high demand and short supply, they are able to affect private ordering in a way that we have not seen before. As a result, they have the potential to be an important check on the high technology sector