HR-CSR Alignment for well-being, Ethical AI in Future Work: A Systematic Review

HR-CSR Alignment for well-being, Ethical AI in Future Work: A Systematic Review

Farhana Akter_, Noor Jannah Afi^*, Linn Shelly Maw, Nusayhah Bibi Peerbuccas, Yasmine Chnayti

School of Business and Social Sciences, Albukhary International University, Alor Setar, Malaysia

^*Corresponding Author’s

DOI: https://dx.doi.org/10.47772/IJRISS.2025.907000315

Received: 30 June 2025; Accepted: 07 July 2025; Published: 15 August 2025

ABSTRACT

This paper critically explores the nexus between ethical artificial intelligence (AI), corporate social responsibility (CSR), and human resource management (HRM) in order to identify practices that enhance the well-being of employees in new-age workplaces. Drawing on 59 peer-reviewed articles (2017–2024), the review synthesizes data with theoretical frameworks such as social exchange theory and psychological contract theory. It captures positive correlations among CSR-HRM practices and job satisfaction, and negative ethical concerns emanating from AI use, i.e., bias, job loss, and emotional distress. Step-by-step ethical AI integration is modeled out, giving an organizational blueprint. The study also reveals major geographic representational gaps—i.e., from Africa, Southeast Asia, and Latin America—and these may limit the applicability of current knowledge worldwide. The research concludes by presenting real-life recommendations for inclusive, sustainable workplaces and suggests that future empirical work using case studies, interviews, and surveys should be done.

Keywords: Human Resource Management, Corporate Social Responsibility, Ethical AI, Employee well-being, Sustainable Workplace.

INTRODUCTION

The convergence of human resource management (HRM), corporate social responsibility (CSR), and artificial intelligence (AI) signals a disruptive age in the future of work. As the world has a digitally globalized economy, organizations will need to push frontier technologies to lead efficiency and competitiveness, and also confront social and ethical obligations (Benbya et al., 2020; Fenwick et al., 2024). AI is revolutionizing HR practices—running from recruitment, performance management, learning and development, to workforce analytics—offering pace, scalability, and improved decision-making accuracy (Palos-Sánchez et al., 2022; García‐Navarro et al., 2024).

However, the ethical use of AI in HRM is facing complex challenges, including risks of algorithmic bias, privacy invasion, and emotional remoteness of employees (Charlwood & Guenole, 2022; Oyekunle et al., 2024). Unless AI technologies are grounded in human-centered and socially conscious philosophies, they have the potential to undermine the trust of employees, decrease autonomy, and increase workplace inequalities. That is where CSR principles shine the light, making sure that AI-enabled HR policies are inspired by values of justice, openness, equity, and sustainability (Alonso-Nuez et al., 2022; Kulkov et al., 2024).

While a lot of research on CSR, HRM, and AI has been done in isolation, there have not been many integrative studies examining their intersectionality, especially in relation to the welfare of employees. Initial research suggests that when AI is integrated into CSR-reliant HRM systems, job satisfaction, psychological safety, and long-term commitment are increased (Pimenta et al., 2024; Gupta et al., 2024). However, issues are present with regards to the moral compromises, stakeholder inclusivity, and transnational applicability of the same.

This study bridges this gap in a systematic review of 59 peer-reviewed articles between 2017 and 2024. It aims to critically investigate the triadic relationship between CSR, HRM, and ethical AI, focusing on their collective influence on employee welfare. Grounded in social exchange and psychological contract theories, this review articulates the key trends, challenges, and opportunities and articulates a step-by-step process for responsible implementation of AI in CSR-based HRM. By focusing on stakeholder engagement and proposing realistic integration channels, this research contributes to sustainable, inclusive, and ethically sound work environments in the digital age.

Research Problem

The confluence between Corporate Social Responsibility (CSR), Human Resource Management (HRM), and Artificial Intelligence (AI) in contemporary organizations, especially with regard to keeping employees happy, has remained unexplored. Much has indeed been done separately on CSR and sustainable HRMs, but a detailed study of the impact of CSR-aligned HR practices on employee well-being in tech-driven contexts does not exist (Madero-Gómez et al., 2023). Moreover, there is a range of questions raised by the ethical implementation of AI in HR processes as to the psychological and emotional effects on the workers despite the efficiency and reduced bias it might create (Charlwood & Guenole, 2022).

Research objectives: 

1. To analyze the impact of CSR-aligned HR practices on employee well-being.
2. To investigate the effects of ethical AI integration on employee well-being.
3. To identify best practices for integrating HR, CSR, and AI strategies to enhance quality.

METHODOLOGY

Research Design

This paper aims to explore the link between CSR, HRM, and the ethical use of AI in enhancing the welfare of workers through a systematic literature review. This study adopts a systematic literature review (SLR) approach to critically examine the intersection of ethical artificial intelligence (AI), corporate social responsibility (CSR), and human resource management (HRM). The SLR method is well-suited for synthesizing fragmented research across disciplines and ensuring methodological rigor and transparency (Tranfield, Denyer, & Smart, 2003).

Literature Search Strategy

A structured search was conducted across two comprehensive academic databases: Scopus and Google Scholar. These platforms were chosen due to their extensive indexing of peer-reviewed journals across social sciences, business, and technology domains. The search strategy employed Boolean operators and keyword combinations such as “ethical AI,” “corporate social responsibility,” “human resource management,” “employee well-being,” “digital transformation,” and “AI in HR.” The inclusion criteria required that studies: (1) were published between 2017 and 2024, (2) were peer-reviewed journal articles, (3) explicitly discussed the intersection of at least two of the three focal areas—AI, CSR, and HRM—and (4) addressed implications for employee experience, ethics, or organizational values.

Out of 6,126 initially retrieved articles, duplicates and irrelevant studies were removed based on title and abstract screening. A total of 187 full texts were assessed for eligibility, leading to a final sample of 59 articles that met all inclusion criteria. The review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to ensure methodological consistency and reproducibility (Moher et al., 2009).

Data extraction involved detailed coding using Mendeley for reference management and NVivo for thematic analysis. Articles were categorized based on thematic domains including: ethical risks and benefits of AI in HRM, CSR-aligned HR practices, stakeholder perspectives, regional focus, and impact on employee well-being. Analytical emphasis was placed on triangulating findings across these themes to identify gaps, trends, and practical implications.

The synthesis process followed an interpretive logic, focusing not just on content aggregation but also on theory-building and framework development. In particular, social exchange theory and psychological contract theory were employed to conceptually ground the emergent relationships between AI technologies, CSR values, and HRM strategies. This approach supports a holistic understanding of how ethical AI integration in HRM can either enhance or undermine organizational sustainability and human-centric goals.

The search procedure involved the following steps:

After the screening process, 27 papers from Scopus and 32 articles from Google Scholar were selected for a comprehensive review. Therefore, this choice allowed for the comprehensive analysis of the literature on the related functions of HRM, CSR, employee well-being, and moral AI practices and provided a solid foundation for further research in these areas.

Figure 1: PRISMA flowchart

The PRISMA flow diagram illustrates the process of selecting the research on HRM, CSR, and ethical AI integration for the purpose of this review. Initially, a total of 6,126 articles were identified (2,526 from Google Scholar and 3,600 from Scopus). 2,637 records were reviewed after 3,489 duplicates had been removed, and 2,522 studies that were considered irrelevant were eliminated. Ten of the 115 complete texts that were requested were not found. 105 full-text papers were evaluated during the eligibility phase, and 46 were disqualified for various reasons, including inadequate detail, publication type, or inappropriate study design. A thorough and pertinent body of data was ultimately ensured by the inclusion of 59 papers in the final review.

Publication Sources: In order to maintain academic integrity, articles were sourced from respectable academic publishers like Wiley, Frontiers, IEEE, Emerald, Sage, MDPI, Taylor & Francis,  Springer, Elsevier, ACM Digital Library, ERA, and others known for their contributions to the HRM and CSR fields.

Publication Date Range: Considering the rapid advancements in CSR practices and AI technologies, preference was given to literature published within the last five to seven years (2017–2024) to ensure the relevance and timeliness of the findings.

Figure 2: Number of articles by publication year

This bar graph depicts the number of research publications used in the year of publication, ranging from the year 2010 to 2024. The use of more current literature is on the rise gradually. In 2010, there was only one publication, which could have been preliminary or preliminary work. There were seven articles by 2017, which demonstrates the growing interest in the topic. One article was used in 2018, which shows a slight decrease in the use of the word. Specifically, there were eight publications in 2020, and it could be attributed to the fact that the early researches were conducted during the COVID-19 pandemic. Furthermore, there are five publications cited from the current year that focus on HR procedures and technology-based work. Moreover, only eight publications were cited in 2022, which is higher than in the previous years. It remained high at seven articles in 2023. A gradual increase in the number of papers used in the research can also be observed, which reaches its highest level in 2024: twenty-two papers are used, which proves the significance and timeliness of the topic in modern academic research.

EXISTING STUDIES RESEARCH GAP

During the last few years, researchers have opened up multiple questions on what’s going on in HRM that connects it to data analytics, AI, and CSR (Bresciani et al., 2021; Fenwick et al., 2024; Santana et al., 2020). Multiple researchers have examined how HRM is important in various settings, and some examine HRM in innovation, family businesses, and sustainability (Flamini et al., 2020; Namen Jotabá et al., 2022). Additionally, researchers have recognized that the emergence of sustainable and green HRM practices has increased, along with AI being used in the main HR practices of performance management, training, and hiring (Chowdhury et al., 2023). Palos-Sanchez et al., 2022).

Nonetheless, there are evident research gaps in this body of work. Research on sustainable HRM and CSR has focused separately on these two distinguished topics; it is unclear how CSR-focused HR practices promote or impede employee well-being, particularly in the fast-paced and technologically evolving workplace. Additionally, while AI HR is being embraced, there has been minimal discourse on the ethics of AI and the impact of AI on employees’ psychological and emotional well-being (Charlwood & Guenole, 2022). The employee-focused outcomes are less evident in the present-day literature than efficiency and performance measures.

In addition, most of the empirical literature examines the domains of CSR, HRM, and AI separately. Thus, there is a chronic lack of integrative studies that combine CSR and HRM activities with the intent of identifying best practices for enhanced employee well-being. Specifically, the intersection of ethical AI, CSR values, and strategic HR practices is an under-explored topic ripe for theoretical and practical contributions.

This research aims to fill these gaps by focusing on the following questions:

• How do CSR-aligned HR practices impact employee well-being across different workplace cultures?
• What are the psychological and emotional effects of ethical AI integration from the employees’ perspective?
• How can organizations holistically integrate HRM, CSR, and ethical AI to build a sustainable and human-centered work environment?

RESULT

Background of the selected articles

This article is based on two influential theoretical frameworks which are Social Exchange Theory and Psychological Contract Theory that offering interpretive lenses through which employee reactions to ethical AI and CSR-HRM practices can be explained. Social Exchange Theory is concerned with the reciprocal relationship between employees and organizations. When HRM practices are established to be socially responsible and fair, employees will exhibit higher levels of commitment, loyalty, and discretionary effort. At the same time, Psychological Contract Theory focuses on implicit promises between employers and employees. Moral failure in AI implementation or CSR failures can break these psychological contracts, resulting in disengagement or resistance. The two theories underscore the importance of open, value-based organizational behavior to preserve worker trust and thriving in technology-enabled workplaces.

This systematic review found 59 relevant publications that explored the relationship between human resource management (HRM), corporate social responsibility (CSR), and ethical AI integration to enhance employee well-being. Besides that, we chose 27 articles from Scopus and 32 from Google Scholar for in-depth analysis following a thorough screening procedure that included identifying duplicates and evaluating relevance based on titles and abstracts. Moreover, thematic analysis of these publications highlighted significant topics such as CSR-driven HR practices, employee well-being strategies, ethical issues of AI in HR, and green/sustainable HRM. All of these concepts were further examined through theoretical frameworks, which include the Social Exchange Theory and the Psychological Contract Theory, in order to gain a deeper understanding. In addition, the review considered research from a range of academic sources, like Springer, Elsevier, Wiley, Sage, MDPI, IEEE, and Taylor & Francis, also the articles were from developed nations such as the US, UK, Canada, Australia, and other European countries, showing the broad interest in integrating ethical HR and AI. However, a Few articles from Asian nations, such as China, India, and Malaysia, as well as the majority of the selected papers, were published within the last five years.

Human Resource Management and CSR: Theoretical Perspectives

According to Espinosa-Jaramillo et al. (2024) and Pham et al. (2024), HRM methods use theories like Social Exchange and Psychological Contract to understand the processes involved, which are crucial in increasing employee engagement with CSR projects. Moreover, to explain how HRM methods influence employee attitudes and behaviours regarding CSR, Social Exchange Theory and Psychological Contract Theory are useful.

Social exchange theory

Socially responsible HRM practices and CSR activities lead to motivation, job satisfaction, business identification, affective commitment, organisational support perceived, and work involvement and retention (Pimenta et al., 2023; Boutmaghzoute & Moustaghfir, 2021; Wang et al., 2017). This principle states that when people interact with others, they prefer to increase benefits and decrease costs. According to Espinosa-Jaramillo et al. (2024), employees and employers enter into a mutually related exchange, putting energy, time, and effort into each other for material and non-material rewards and benefits. Incorporating extraneous CSR activities with certain tasks of employee productivity and staff retention augments further when they are associated with certain HR techniques like performance-based pay, recruitment based on fit, and lengthy training (Bang et al., 2022; De Silva, K. M., & De Silva Lokuwaduge, 2021).

Psychological contract theory

Psychological Contract Theory deals with the implicit expectations and obligations that exist between organizations and employees (Espinosa-Jaramillo et al., 2024; Farnese et al., 2018). Fang et al. (2021) and Lu et al. (2020) also demonstrate further that CSR behaviors positively impact work performance, with psychological contract fulfillment as the mediator. Topa et al. (2022) and Roeck et al. (2023) explain how CSR can create loyalty but reduce perceived corporate dishonesty and negative employee reactions to psychological wrongdoing. In addition, it has been found that the use of green HRM practices has a positive effect on pro-environmental psychological climate and behavior, which in turn gives rise to green CSR (Hameed et al., 2022; Zihan et al., 2024). For example, the exchange of information and psychological contracts has a significant effect on employee performance and the provision of nonstandard services in outsourcing companies (Munawir & Suseno, 2024).

Figure 3: Integrating social exchange and psychological contract theories in HRM and CSR

The growth of ethical AI in the workplace

The impact of AI on the workplace is likely to be uneven and dependent on several factors such as location, economic activity, gender, and level of education, as well as concerns about job displacement (Clifton et al., 2020). According to Akinnagbe (2024), increasing focus on ethical integration, employee rights, and the future of human-AI collaboration, AI is expected to become an important tool in modern workplaces by 2025. According to some predictions, up to 30% of employees may work remotely full-time by 2030 (Johnson, 2020; Skidelsky, 2020). However, it is still unclear how much AI will impact labour demand and work structures (Clifton et al., 2020). Further, Facebook recently announced that by 2030, half of its employees may work remotely full-time. Microsoft created a roadmap for its global workforce after identifying the necessity for manager and team training on remote work (Skidelsky, 2020). Likewise, more recent data from 2023–2024 shows the use of AI is projected to exceed 6.6% by the fall of 2024, up from 3.7% to 5.4% (Bonney et al., 2024).

Figure 4: The growth of the AI use rate

This graph shows that AI use rates increased from 3.7% to 5.4% in 2023–2024, with an expected 6.6% by the autumn of 2024 (Bonney et al., 2024).

AI, HR, and CSR in the Workplace: Trends and Ethical Considerations

Artificial intelligence (AI) is transforming workplace practices by integrating it into human resource management (HRM), which requires a strategic, ethical approach in accordance with corporate social responsibility (CSR) policies (Ali Fenwick et al., 2024; Bubicz & Ferasso, 2024). Moreover, HR is crucial to boost CSR frameworks due to the fact that it is a strong factor in influencing employee motivation and organisational culture (Bristol-Alagbariya et al., 2023). It requires a people-centric approach and currently exists between humans and computers due to the huge application of AI in HR responsibilities, which shows organisations with a chain of ethical, people, and technological issues (Ali Fenwick et al., 2024). Bubicz and Ferasso (2024) believe that there is a model of maturity regarding proper AI application in HRM that is centered on accountability, transparency, as well as employees’ empowerment. Furthermore, HR’s strategic stance needs to evolve from an ‘inside-out’ perspective towards an ‘outside-in’ perspective, while recognizing the importance of stakeholder’s needs, so that aspects of social responsibility (CSR) can be coordinated with corporate goals (Mhatre & Dhole, 2017). The alliance of AI and CSR impacts the overall organisational effect, productivity metrics and how employees are managed (Bristol-Alagbariya et al., 2023; Mhatre & Dhole, 2017).

Meanwhile, AI-enabled technologies such as machine learning and natural language processing are also being leveraged to maximize HR development and assess employee engagement (García‐Navarro et al., 2024; Ekuma, 2023). The significant role of organization-led sustainability goals is evidenced by the rapidly growing role of corporate social responsibility (CSR) as a pathway for engaging in improved workplace health due to COVID-19 (Alonso-Nuez et al., 2022). Moreover, the UN Sustainable Development Goals (SDGs) reiterate the value of strategic alignment, change management, and the build-out of technical infrastructure, especially with regard to applying AI in the context of CSR (Kulkov et al., 2023). Overall, technological advancements allow for a combination of AI with CSR to embrace integrative HR processes, enhance overall employee engagement, and help achieve organizational sustainability. Furthermore, AI technologies are fast becoming key processes in sectors such as manufacturing, banking, and customer service, shifting human work to more critical positions, and removing a great deal of tedious labor (Farhang Mossavar-Rahmani & Zohuri, 2024; Anurag et al., 2023). Some of the benefits of AI are cost reductions and more strategic decision-making. While AI has the potential to improve efficiencies and productivity, it will also reduce employment and increase social injustices (Badhurunnisa & Dass, 2023; Anurag et al., 2023). We are also presented with moral issues, such as privacy, bias, and accountability (Oyekunle et al, 2024). Organizations need to navigate challenges such as data privacy, system comprehensiveness, and decision-making transparency (Syifa, 2024; Badawy et al., 2024) with respect to AI. Organizations must effectively balance and measure, the potential of AI against their moral stance toward human rights and dignity (Oyekunle et al., 2024).

It is clearly seen that AI technologies contribute to the automation of previously complex processes, thus improving the capabilities of a person. Over the years, they have manifested themselves as critical tools in many industries, including manufacturing, banking, and customer service (Farhang Mossavar-Rahmani & Zohuri, 2024; Anurag et al., 2023). Moreover, several Advantages of Artificial Intelligence include the enhancement of decision-making and cost-cutting. While AI brings out the prospect of enhancing efficiency and production, it also holds drawbacks of inequality and disemployment (Badhunissa & Dass, 2023; Anurag et al., 2023). Privacy issues, bias, and accountability are a few of the ethical implications of technological advancement (Oyekunle et al., 2024). In addition to this, other issues such as data privacy, system integrity, and decision-making transparency should be accorded the highest level of importance by organisations as a way of mitigating these challenges (Syifa, 2024; Badawy et al., 2024). AI and all its potential have to be aligned with human rights and human dignity (Oyekunle et al., 2024). For the implementation to be effective, organizational goals should be achieved in opening up communication lines and staff training in a bid to reduce the likelihood of job losses (Borkovich et al., 2024). According to Borkovich et al. (2024), they need to apply a double-edged philosophical approach aimed at improving trust between the workers and top administration for an organization to flourish. To enhance ethical AI in the workplace, regular checkups, updates, and reforms in policies, and ethical education are important (Syifa, 2024).

Employee Well-being and Importance in HR

In the current world of work, the successful implementation of human resource management practices is found to rely highly on the health and motivation of a worker (Elufioye et al., 2024; Patterson et al., 2010). Consequently, the well-being of employees ensures that it is respected and valued for its correlation with performance and positivity in the workplace, as attributed by Kowalski and Loretto (2017). Gupta, 2024). To meet this, the following strategies have been used by HRM in enhancing well-being at the workplace: There has been adoption of flexible work schedules, wellness programs, and EAPs (Gupta, 2024). Guest, 2017). However, such initiatives require organisational culture, management support, and engagement of employees (Gupta, 2024; Lucas et al., 2020). In addition to the previous points, data analytics and technology are applied to well-being initiatives (Elufioye et al., 2024). However, these problems, including inadequate funds, resistance to change, and a negative stigma towards mental health, still exist (Gupta, 2024; Elufioye et al., 2024). But today, HR departments are learning rapidly what it takes to cultivate a culture of WWPS through intervention and management of employee mental health illness and more open conversations (Elufioye et al., 2024). Therefore, it is stressed that to promote the enhancement of people-oriented organisational culture, the above-mentioned activities have to be closely supervised and improved (Elufioye et al., 2024).

According to Madero-Gómez et al. (2023) pointed out that both individuals and organisations can also benefit from sustainability-based HRM, especially when it supports environmental sustainability and employees’ well-being (Madero-Gómez et al., 2023). For instance, in the Malaysian service sector, the analysis showed that the motivation and the opportunity-enhancing HR policies directly affected the well-being of the employees during the COVID-19 epidemic (Johar et al., 2022). Introducing the theoretical framework of social values for understanding well-being at work, a Portuguese research study revealed that respect, trust, equity, help, and appreciation are the most significant and shared values (Santos & Lousã, 2022). It can be concluded from these findings that sustainability should be integrated into developed organizations’ HR processes with consideration of employees’ welfare. To this extent, in an attempt to maintain the continued importance of HRD, the Academy of Human Resource Development (AHRD) has noted the importance of the well-being paradigm (Alagaraja, 2023). 

Table 1: Literature Review Matrix

DISCUSSION

The results of this systematic review are consistent with emerging evidence that corporate social responsibility measures in HRM positively influence the well-being of employees. Repeated findings from many studies emphasize that fit-based recruitment as a socially responsible HR measure helps to reduce the wage gap (Espinosa-Jaramillo et al., 2024; Pimenta et al., 2023; Boutmaghzoute & Moustaghfir, 2021). There are several studies that show how the activities of HRM are being changed by AI, resulting in greater efficiency, greater objectivity, and more customized outcomes. (Fenwick et al., 2024; Palos-Sanchez et al., 2022; Vishwanath & Vaddepalli, 2023). But significant ethical questions are at the heart of the debate. There is a need to pay attention to reduce the risks of job displacement, privacy breaches, algorithmic bias, and the decline in traditional, human-oriented work cultures (Charlwood & Guenole, 2022; Oyekunle et al., 2024). Furthermore, according to Bubicz and Ferasso (2024), frameworks highlight the need for greater transparency and accountability in the manner AI use should be kept ethical.

There are both benefits and drawbacks to the application of AI, HRM, and CSR approaches. It is interesting to note that AI is capable of enhancing hiring diversity, as well as the development of personalized health programs, both of which contribute to CSR goals. AI applications that do not reflect an appropriate course of ethical alignment can impede CSR goals by the erosion of trust and perpetuation of possible disparities in the workplace. These studies have noted the lack of discourse on the active relevance between AI and CSR in practice, even though previous research has examined each issue separately. It is also imperative in any study and ethical, lasting outcomes that there is management support, cultural transparency, and ongoing stakeholder engagement (Alonso-Nuez et al., 2022; Bristol-Alagbariya et al., 2023).

Proposed Framework: Ethical AI Adoption in CSR-HRM

For the operationalization of adopting ethical AI in CSR-focused HRM, this study proposes a six-step implementation framework. First, organizations must conduct an analysis of their current HRM-AI landscape to identify strengths, weaknesses, and ethical risks. Second, stakeholder mapping must be conducted for inclusive participation by various stakeholders like employees, policymakers, IT developers, and labor unions. Thirdly, there should be a CSR alignment test to check the alignment of AI practices with organizational ethical commitments and values. Fourthly, meticulous risk-benefit analysis should be performed based on ethical impact matrices considering social, legal, and psychological impacts. Fifthly, AI systems should be implemented with transparency, user training, and fairness auditing in mind. Finally, mechanisms for continuous monitoring and feedback must be established to adjust policies and avoid unintended consequences. This sequential framework is a practical model for fair, sustainable, and human-centered AI deployment in HR functions.

Table 2: Summary Matrix: AI in HRM within CSR Context

This literature review indicates that while ethical AI can transform HRM and facilitate CSR objectives, it must be managed carefully and with ethical anticipation. AI technologies are not value-neutral; their design and deployment are guided by priorities and assumptions of developers. Organizations that desire to promote sustainability and the well-being of workers must include ethical aspects in every step of AI adoption. Business communication transparency, ongoing training of employees, and employee input are necessary to facilitate acceptance and trust. In addition, organizational leaders need to transition from an operationally oriented HR view to one based in values that acknowledges the interdependence of technology, ethics, and human dignity. Despite these findings, sparse empirical evidence and geographic clustering of existing studies indicate that there is a requirement for more and varied evidence.

Limitations of the Study

Despite making valuable theoretical contributions, this study is also bound by some significant limitations that circumscribe the scope and applicability of its findings. First, the geographical coverage of the literature included is strongly skewed toward high-income regions namely Europe, North America, and parts of Asia. This limited geographical coverage might hide culturally distinctive dilemmas and prospects of integrating ethical AI into CSR-driven HRM practices in underrepresented areas such as Africa, Southeast Asia, and Latin America. The lack of studies from these contexts weakens the generalizability of findings and demands cautious extrapolation of results across the globe.

Second, the absence of empirical testing is a significant gap. The review rests primarily on conceptual models and theoretical integration. Although this is appropriate for exploratory observations and framework development, it is not tested through real application. Empirical research techniques, i.e., case studies, ethnography, and large surveys, are needed to test how ethical AI adoption actually influences HRM outcomes and employees’ well-being.

Third, the review is limited by the narrow scope of stakeholder perspectives. Most of the selected studies represent organizational or managerial perspectives, often omitting lived experiences of employees, policymakers, unions, and civil society organizations. Ethical use of AI in HRM cannot be fully understood without including these voices, particularly from marginal or vulnerable groups. Future research must widen stakeholder involvement to create inclusive, equitable outcomes.

Finally, the review does not comparatively examine AI-driven risks and benefits by different organizational settings or sectors. The lack of comparative matrices or scenario-based models limits the degree of granularity of recommendations. The incorporation of these analytical frameworks in future research may allow organizations to tailor ethical AI frameworks according to sectoral risks, business models, and CSR maturity levels.

RECOMMENDATIONS

Companies should implement CSR ideas into their basic human resources (HR) strategies, such as hiring, training, and performance evaluations, in order to promote a moral and just culture that increases employee productivity and participation. On top of that, established regulations that ensure equity, privacy, and bias decrease, AI tools must be implemented transparently and morally. Further, using ethical AI will help to minimize potential inequalities; HR employees should be engaged in AI ethics. Active employee participation in AI adoption and CSR projects may improve approval and benefits, continuous feedback systems can help understand the psychological and emotional implications of these initiatives, allowing for constant enhancements. Apart from that, organisations ought to promote sustainable and green HR practices to boost employee well-being and develop their initiatives to environmental sustainability, which follow international goals. After that, technology and social responsibility should be successfully united, because of strong management commitment and the improvement of an honest and trustworthy culture. Besides that, maintaining ethical and equal procedures and allowing businesses that help to continuously adapt and improve requires ongoing evaluation of AI’s effects on workplace dynamics and employee well-being. As a result, using these strategies, companies can establish moral, inclusive, and sustainable work environments that support employee well-being and foster long-term success in the constantly evolving workplace.

Future research must prioritize empirical methods to vet the theoretical findings of this study. Case studies across industries and geographies—particularly in Africa, Southeast Asia, and Latin America—are necessary to enhance cross-cultural understanding. Surveys and interviews with employees, HR professionals, AI developers, and policy stakeholders would provide fine-grained perspectives on the daily difficulties and successes of ethical AI implementation. Long-term studies can analyze the impact of CSR-driven AI-HRM systems on employee well-being in the long term. Further, interdisciplinary research collaboration between management sciences, data ethics, psychology, and labor law would increase the soundness of future frameworks. Sectoral applications must also be explored by researchers to identify how ethical AI adoption varies across sectors such as healthcare, education, manufacturing, and the gig economy.

CONCLUSION

To create a productive and ethical working environment, organisations should combine CSR-oriented HRM practices with a heavy emphasis on employee well-being. Developer involvement in AI technologies can be the most effective way to achieve maximum benefits and minimal negative impacts of the adoption of AI. Continued support for employment and frequent audit of the effects of AI on workplace dynamics is a requirement for the successful implementation of these advances. AI initiatives that meet the ethics and corporate social responsibility (CSR) requirements can be useful for business and for the whole society.

The ethical integration of AI and a CSR-inked HRM strategy has brought better organisational results and raised the level of employee job satisfaction. Studying responsible HRM and CSR practices, we can see that they help to increase the engagement and productivity of employees in various industries such as the ones analyzed in the framework of the current research. Any further research in this field is expected to be based on exploring the long-term effects of AI and CSR integration and pointing out the proper measures of enhancing employee well-being and promoting sustainable organisational improvement. To better understand the transformative effect of AI and CSR on the future of work, other industries would also need to be researched.

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