Socially Responsible Artificial Intelligence Empowered People Analytics: A Novel Framework Towards Sustainability

by Yu-Ling Chang and Jie Ke

In today’s data-driven world, Artificial Intelligence (AI) and People Analytics are shaping how organizations understand and manage their workforce. But what do these terms really mean, and how do they impact our workplaces? Let’s break it down in simple terms.

What Is People Analytics and AI-enabled People Analytics?

People Analytics is like having a magnifying glass for your organization. It involves collecting and analyzing data about employees to gain insights into their behavior, performance, and well-being. Think of it as detective work, but instead of solving crimes, we’re uncovering patterns in employee data. With the enablement of AI, AI-enabled People Analytics can save human resource (HR) teams significant time and effort and improve the efficiency of the HR process, leading to better people decisions and also advance people analytics from HR or operational reporting to “true analytics” that can achieve predictive and prescriptive HR intelligence and contribute strategic values to organizations.

The Dark Side of AI in People Analytics

But wait, there’s a twist! AI isn’t all rainbows and unicorns. Many renowned organizations as well as the big techs such as Amazon, Facebook, and Google have been reported being biased and discriminated in their use AI for people decisions including applicant screening, job advertisements, and education pricing. These incidents have shown how AI-enabled People Analytics can potentially lead to discriminatory consequences and pose organizations in risks if we don’t use it right. Nevertheless, existing People Analytics literature has been overly optimistic about AI while overlooking critical ethical concerns and risks may lay within. We therefore proposed the concept of socially responsible AI (SRAI) in our recent study. Ethical concerns and risks associated with AI in People Analytics cannot be overlooked, making it crucial to implement Socially Responsible AI in People Analytics to ensure sustainability within and beyond organizations. Our current study aims to bridge the gap in existing literature by providing guidance to HR practitioners on implementing Socially Responsible AI in People Analytics

A Novel Framework of Socially Responsible AI

Despite the growing interest in Socially Responsible AI, research on the topic is scattered and lacks a complete understanding. To address this, we conducted an extensive review of existing literature to develop a comprehensive framework for Socially Responsible AI as depicted in the following figure. This framework connects AI with important sustainability concepts such as corporate social responsibility (CSR), environment, social, and governance (ESG), and UN sustainable development goals (SDGs). The resulting framework is presented as an inverted pyramid model with five distinct hierarchical levels: Economic, Legal, Ethical, Philanthropic, and Environmental. The inverted pyramid structure of our model highlights the increasing importance and the time-lasting impact on a wider range of stakeholders from the bottom to top. It was also found that the SDGs are insufficient in addressing the ethical and legal concerns, as they have fewer goals specifically related to these two critical aspects. Moreover, our comprehensive framework of Socially Responsible AI covers several competing concepts including Robust AI, Lawful AI, Ethical AI, Human-Centered AI, and Sustainable AI. We hope this comprehensive framework can help to advance the field’s understanding of Socially Responsible AI and guide the further theory building efforts.

Moving from AI-enabled PA to Socially Responsible AI -Empowered PA Towards Sustainability

To operationalize the Socially Responsible AI framework in the context of people analytics in a comprehensive fashion, the second takeaway of this research is to provide recommendations for HR practices to address the identified requirements and considerations for SRAI of various stakeholders, guiding HR professionals to implement Socially Responsible AI -empowered People Analytics towards sustainability. To achieve this goal, we adopt the queries of responsible AI algorithms (SRAs) to identify the major Socially Responsible AI stakeholders (subjects) and analyzed their essential considerations (objectives), the impediments of AI technology (causes), and the solutions (means) of Socially Responsible AI in the context of People Analytics. Our findings support HR professionals in understanding Socially Responsible AI and further implementing Socially Responsible AI-empowered People Analytics to enable human resource development (HRD) activities including learning and development, coaching, knowledge management, career planning, employee wellness program, performance and change management, employee engagement and retention, succession planning, and workforce planning. The implications pervade every facet of HRD, including training and development (TD), career development (CD), and organization development (OD).

Conclusion

AI-enabled People Analytics offers valuable insights for HR decision-making, but it must be approached responsibly. Implementing Socially Responsible AI-empowered People Analytics enhances HRD activities while upholding ethical values and preventing discrimination. The comprehensive framework of Socially Responsible AI serves as a guide for organizations, emphasizing economic, legal, ethical, philanthropic, and environmental considerations. By adopting Socially Responsible AI-empowered People Analytics, HRD professionals play a crucial role in harmonizing ethical values, safeguarding against discrimination, and promoting transparency. This approach ensures that AI in People Analytics contributes to sustainable and responsible HRD practices.

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Article Details
Socially Responsible Artificial Intelligence Empowered People Analytics: A Novel Framework Towards Sustainability
Yu-Ling Change and Jie Ke
First Published September 11, 2023 Research Article
DOI: 10.1177/15344843231200930
Human Resource Development Review

About the Authors

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Yu-Ling Chang, Ph.D., GCDF, is an HR analyst at the Taiwan Semiconductor Manufacturing Company Limited (TSMC). Dr. Chang earned her PhD in Workforce Education and Development and Counselor Education at Penn State University, with a special concentration in Human resource development (HRD), organization development (OD), and career development. With degrees in economics and psychology plus twelve years of practical experience in talent assessment, training and development, Dr. Chang's diverse expertise enables her to converge multidisciplinary knowledge in advancing the field of HRD and OD. Dr. Chang's recent work focuses on using data-driven approaches and artificial intelligence (AI) to empower people and organizations to change, grow and excel as unique beings. Her topics cover socially responsible AI (SRAI), explainable AI (XAI), and AI for human good in modern organization contexts.

https://www.linkedin.com/in/yu-ling-chang/

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Jie Ke, Ph.D., is an associate professor of HRD at Jackson State University. She earned her Ph.D. in HRD from Texas A&M University and holds an MBA from EDHEC in France. Her research interests include eLearning effectiveness, workplace ethics, sustainable HRD/HRM/HR, Chinese/International HR Development/Management, and women's leadership. Dr. Ke co-edited the first book on Chinese women's leadership and has served on the Board of Directors, and Co-chaired the China SIG and the International Committee for AHRD. She has been an external evaluator for two NSF grant projects and is currently leading two NSF-funded projects on AI-assisted enhancement and evaluation of STEM students’ learning at HBCUs.