Associate Professor of Human Resource Development Jackson State University Jackson State University
Abstract: Introduction and Problem Statement
Artificial Intelligence (AI) has been increasingly implemented to support or even automate a wide range of human resource development (HRD) processes such as evaluating or estimating employee performance, engagement, attrition, and turnover, investigating important characteristics of high-performance employees for decisions in recruitment, talent management, task-allocation , and customized training or coaching (Buck & Morrow, 2018; Graßmann & Schermuly, 2021; Haesevoets et al., 2021; Yoon, 2018). However, the merits of economical effectiveness and efficiency brought by AI should not be the reason to shed corporate social responsibilities (CSR) (Zhao, 2018). Critical social issues involving ethics, bias, equity, privacy, policy, and governance have been identified due to socially indifferent AI in organizations and affect various stakeholders (Akter et al., 2021; Frey & Osborne, 2017; Garcia-Arroyo & Osca, 2019; Speer, 2021). Yet scant work in the field of HRD has explored how CSR can be attended when HRD towards more implementations of AI.
Socially Responsible AI and Theoretical Frameworks
Socially Responsible AI (SRAI) in organizational contexts presents a new concept that connects AI to CSR (Cheng et al., 2021). It takes a human-centered perspective to prioritize the needs of all stakeholders while mitigating the negative consequences that would affect them. The needs were configured within the seminal CSR pyramid framework (Carroll, 1991) on different levels: economic (be profitable), legal (obey the law), ethical (be ethical), and philanthropic (be a good corporate citizen) responsibilities. Moreover, to understand SRAI, Cheng et al. (2021) suggested analyzing AI implementations through a framework containing subjects, causes, means, and objectives. The subjects refer to the stakeholders affected by the AI implementations, such as the organizations, supervisors, and current or potential employees. The causes are those result in societal problems such as measuring errors, bias, and privacy. The means are algorithmic methods that can prevent, inform, or mitigate potential risks and harm to the stakeholders and maximize long-term benefits. The objectives are human and social values such as trust, fairness, transparency, safety, and sustainability.
Research Purpose and Questions
As a fundamental step in introducing SRAI to HRD, the purpose of this study is to explore current knowledge on SRAI and map the plan of implementing SRAI in HRD, in the hope of advancing the field’s understanding of SRAI and equipping HRD professionals with the knowledge and tools to address imprudent implementations of AI in HRD. To achieve this purpose, the SRAI frameworks of Carroll (1991) and Cheng et al. (2021) are used to guide our study and four research questions are developed: RQ1. Who are the major stakeholders when implementing AI in HRD? (Subjects); RQ2. What are the needs of the major stakeholders in implementing AI in HRD and where are the needs in Carroll’s (1991) CSR pyramid? (Objectives); RQ3. What may result in AI in HRD failing to attend to the identified needs? (Causes); RQ4. What types of algorithmic methods can address the identified needs? (Means).
Research Design and Methods
The study will conduct a systematic review of SRAI literature in multiple disciplines to identify SRAI issues in HRD and solutions from a multi-stakeholder view. Considering the interdisciplinary nature of the study, the literature search leverages not only the traditional social science databases such as ABI/INFORM, EBSCO Business Source Premier, JSTOR, ERIC, and PsycINFO but also extends to Google Scholar and Web of Science which cover more references in natural science. The following keywords and combinations of keywords are used to search in the literature title or abstracts: socially responsible AI, SRAI, Corporate social responsibility, CSR, artificial intelligence, AI, human resource development, HRD, Human Resource Management, HRM, Human Resources, HR, stakeholder(s) and sustainability. Only work within the past 10 years will be included for review and analysis unless it is foundational. Literature written in non-English languages and those not situated in organizational contexts will be excluded. The collected literature will be reviewed and analyzed by the research team using Nvivo software to conduct coding and theme generation to answer the research questions. To ensure inter-rater reliability of the study analysis, the research team will first develop a codebook together to guide the coding process and then meet regularly during the data analysis phase to resolve questions and conflicting perspectives to reach a consensus on the study results.
Expected Outcomes and Contributions to HRD
The study contributes to HRD by introducing SRAI as a new concept and area for future research and interdisciplinary cooperation, providing theory-based guidance to HRD researchers and practitioners in implementing SRAI to ensure the sustainable development of organizations.
