Developing Fairness Rules for Talent Intelligence Management System

Talent management is an important business strategy, but inherently expensive due to the unique, subjective, and developing nature of each talent. Applying artificial intelligence (AI) to analyze large-scale data, talent intelligence management system (TIMS) is intended to address the talent management problems of organizations. While TIMS has greatly improved the efficiency of talent management, especially in the processes of talent selection and matching, high-potential talent discovery and talent turnover prediction, it also brings new challenges. Ethical issues, such as how to maintain fairness when designing and using TIMS, are typical examples. Through the Delphi study in a leading global AI company, this paper proposes eight fairness rules to avoid fairness risks when designing TIMS

Artificial Intelligence in Human Resource Management: Advancements, Implications and Future Prospects

The present condition, challenges, and potential applications of artificial intelligence (AI) in human resource management (HRM) are all explored in this survey article. As an innovation, artificial intelligence (AI) has the potential to completely revolutionize several facets of human resource management (HRM). Examining the usage of AI-powered tools and systems in different HR processes, the present situation with AI in HRM is examined. These encompass learning and development, performance management, employee engagement, and recruiting. The use of AI algorithms and machine learning approaches to automate regular HR operations, analyze vast amounts of employee data, and provide insightful data to aid decision-making is addressed in this article. However, integrating AI into HRM also poses a number of difficulties that must be resolved. Bias, privacy issues, and transparency are just a few of the ethical and legal ramifications of using AI in decision-making processes that are discussed in this survey. The study emphasizes how accountability and fairness must be maintained in AI systems by responsible design, oversight, and periodic evaluation. With an emphasis on job displacement and workforce reorganization, the possible influence of AI on the human workforce is also explored. To effectively traverse this change, strategies including work role redefinition, employee up skilling, and establishing a collaborative atmosphere between humans and AI are suggested. The possible advantages and breakthroughs that AI might bring to HRM practices are highlighted as the future perspectives of AI in HRM are examined. As new applications for AI in HRM, sentiment analysis, predictive analytics, intelligent decision support, and personalized employee experiences are all highlighted. In order to fully realize the promise of AI in HRM, the study underlines the significance of data infrastructure, data governance frameworks, and a data-driven culture. Overall, this survey study offers an in-depth review of the existing situation, difficulties, and prospects for AI in HRM. It aggregates current information, identifies research gaps, and gives practitioners and scholars new perspectives on how AI will fundamentally alter the way HRM activities are carried out in the future

Coming Out of the Dungeon: Mathematics and Role-Playing Games

After hiding it for many years, I have a confession to make. Throughout middle school and high school my friends and I would gather almost every weekend, spending hours using numbers, probability, and optimization to build models that we could use to simulate almost anything. That’s right. My big secret is simple. I was a high school mathematical modeler. Of course, our weekend mathematical models didn’t bear any direct relationship to the models we explored in our mathematics and science classes. You would probably not even recognize our regular gatherings as mathematical exercises. If you looked into the room, you’d see a group of us gathered around a table, scribbling on sheets of paper, rolling dice, eating pizza, and talking about dragons, magical spells, and sword fighting. So while I claim we were engaged in mathematical modeling, I suspect that very few math classes built models like ours. After all, how many math teachers have constructed or had their students construct a mathematical representation of a dragon, a magical spell, or a swordfight? And yet, our role-playing games (RPGs) were very much mathematical models of reality — certainly not the reality of our everyday experience, but a reality nonetheless, one intended to simulate a particular kind of world. Most often for us this was the medieval, high-fantasy world of Dungeons & Dragons (D&D), but we also played games with science fiction or modern-day espionage settings. We learned a lot about math, mythology, medieval history, teamwork, storytelling, and imagination in the process. And, when existing games were inadequate vehicles for our imagination, we modified them or created new ones. In doing so, we learned even more about math. Now that I am a mathematics professor, I find myself reflecting on those days as a “fantasy modeler” and considering various questions. What is the relationship between my two interests of fantasy games and mathematics? Does having been a gamer make me a better mathematician or modeler? Does my mathematical experience make me a better gamer? These different aspects of my life may seem mostly unconnected; indeed, the “nerd” stereotype is associated with both activities, but despite public perception, the community of role-players includes many people who are not scientifically-minded. So we cannot say that role-players like math, or math-lovers role-play, because “that is simply what nerds do.” To get at the deeper question of how mathematics and role-playing are related, we first need to look at the processes of gaming, game designing, and modeling

When Do Customers Perceive Artificial Intelligence as Fair? An Assessment of AI-based B2C E-Commerce

Artificial intelligence (AI) enables new opportunities for business-to-consumer (B2C) e-commerce services, but it can also lead to customer dissatisfaction if customers perceive the implemented service not to be fair. While we have a broad understanding of the concept of fair AI, a concrete assessment of fair AI from a customer-centric perspective is lacking. Based on systemic service fairness, we conducted 20 in-depth semi-structured customer interviews in the context of B2C e-commerce services. We identified 19 AI fairness rules along four interrelated fairness dimensions: procedural, distributive, interpersonal, and informational. By providing a comprehensive set of AI fairness rules, our research contributes to the information systems (IS) literature on fair AI, service design, and human-computer interaction. Practitioners can leverage these rules for the development and configuration of AI-based B2C e-commerce services

An overview of research on human-centered design in the development of artificial general intelligence

Abstract: This article offers a comprehensive analysis of Artificial General Intelligence (AGI) development through a humanistic lens. Utilizing a wide array of academic and industry resources, it dissects the technological and ethical complexities inherent in AGI's evolution. Specifically, the paper underlines the societal and individual implications of AGI and argues for its alignment with human values and interests. Purpose: The study aims to explore the role of human-centered design in AGI's development and governance. Design/Methodology/Approach: Employing content analysis and literature review, the research evaluates major themes and concepts in human-centered design within AGI development. It also scrutinizes relevant academic studies, theories, and best practices. Findings: Human-centered design is imperative for ethical and sustainable AGI, emphasizing human dignity, privacy, and autonomy. Incorporating values like empathy, ethics, and social responsibility can significantly influence AGI's ethical deployment. Talent development is also critical, warranting interdisciplinary initiatives. Research Limitations/Implications: There is a need for additional empirical studies focusing on ethics, social responsibility, and talent cultivation within AGI development. Practical Implications: Implementing human-centered values in AGI development enables ethical and sustainable utilization, thus promoting human dignity, privacy, and autonomy. Moreover, a concerted effort across industry, academia, and research sectors can secure a robust talent pool, essential for AGI's stable advancement. Originality/Value: This paper contributes original research to the field by highlighting the necessity of a human-centered approach in AGI development, and discusses its practical ramifications.Comment: 20 page

Spot Your Leadership Style – Build Your Leadership Brand

The purpose of the research paper is to present various leadership styles with illustrations of international leader types. It helps the reader spot a particular leadership style for building a leadership brand. It attempts to motivate senior level leaders to appreciate what style of leadership is essential in the current scenario

“Where’s the I-O?” Artificial Intelligence and Machine Learning in Talent Management Systems

Artificial intelligence (AI) and machine learning (ML) have seen widespread adoption by organizations seeking to identify and hire high-quality job applicants. Yet the volume, variety, and velocity of professional involvement among I-O psychologists remains relatively limited when it comes to developing and evaluating AI/ML applications for talent assessment and selection. Furthermore, there is a paucity of empirical research that investigates the reliability, validity, and fairness of AI/ML tools in organizational contexts. To stimulate future involvement and research, we share our review and perspective on the current state of AI/ML in talent assessment as well as its benefits and potential pitfalls; and in addressing the issue of fairness, we present experimental evidence regarding the potential for AI/ML to evoke adverse reactions from job applicants during selection procedures. We close by emphasizing increased collaboration among I-O psychologists, computer scientists, legal scholars, and members of other professional disciplines in developing, implementing, and evaluating AI/ML applications in organizational contexts