Artificial Intelligence and Corporate Social Responsibility: Implications for Sustainable Business Practices
DOI:
https://doi.org/10.64882/ijrt.v14.iS1.969Keywords:
Artificial Intelligence, Corporate Social Responsibility, Sustainable Business Practices, Business Sustainability, Ethical GovernanceAbstract
With the increasing adoption of Artificial Intelligence (AI) in the context of business and society, Corporate Social Responsibility (CSR) is also going through a transformation process: from what it means to how it is viewed or applied. With businesses coming under more and more scrutiny around environmental, ethical, and social aspects of their operations, AI is being portrayed as a strategic weapon to make these corporate programs more effective, transparent, and accountable. This paper investigates the potential contribution of Artificial Intelligence (AI) towards Corporate Social Responsibility (CSR) and how the latter is expected to drive sustainable business conduct. The authors leverage a discursive analysis of secondary literature (academic research, global policy frameworks, and sustainability reports) to investigate the role that AI plays as it relates to environmental sustainability, ethical governance, stakeholder engagement, and responsible supply chain management. At the same time, it provides a critical overview of key issues concerning algorithmic bias, data protection, and individual accountability, along with the potential for corporate social responsibility to be shallowly adopted. Theoretical and Practical Implications The paper posits that, late last decade, it could be determined that, despite the potential gains for sustainability to be made from AI deployment in organizations (and this was noted at a relatively early stage), such beneficial outcomes of such are contingent upon responsible governance, ethical design, and alignment with core CSR principles. According to this analysis, adding AI to the CSR framework will lead towards sustainable business in the long run, if technological change occurs by keeping social responsibility and ethical value demands.
References
Agarwal, C., & Rai, P. (2025). Environmental wisdom and sustainable practices. In Bridging ideas: A multidisciplinary approach to knowledge and innovation (pp. 76–85). Eagle Leap Printers and Publishers Pvt. Ltd.
Bocken, N. M. P., Short, S. W., Rana, P., & Evans, S. (2014). A literature and practice review to develop sustainable business model archetypes. Journal of Cleaner Production, 65, 42–56. https://doi.org/10.1016/j.jclepro.2013.11.039
Brown, T. J., & Dacin, P. A. (1997). The company and the product: Corporate associations and consumer product responses. Journal of Marketing, 61(1), 68–84. https://doi.org/10.1177/002224299706100106
Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W. W. Norton & Company.
Carroll, A. B. (1991). The pyramid of corporate social responsibility: Toward the moral management of organizational stakeholders. Business Horizons, 34(4), 39–48. https://doi.org/10.1016/0007-6813(91)90005-G
Eccles, R. G., Ioannou, I., & Serafeim, G. (2014). The impact of corporate sustainability on organizational processes and performance. Management Science, 60(11), 2835–2857. https://doi.org/10.1287/mnsc.2014.1984
European Commission High-Level Expert Group on Artificial Intelligence. (2019). Ethics guidelines for trustworthy artificial intelligence. European Commission. https://digital-strategy.ec.europa.eu
Floridi, L., Cowls, J., Beltrametti, M., et al. (2018). AI4People: An ethical framework for a good AI society. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
Jaiswal, A., Mishra, A., Kumari, K., Singh, P., & Agarwal, C. (2025). The impact of analyzing employee development on talent acquisition. In The canvas of knowledge: A multidisciplinary approach (pp. 122–126). Eagle Leap Printers and Publishers Pvt. Ltd.
Janssen, C., & Vanhamme, J. (2006). Towards a dynamic process model of consumer resistance: An empirical investigation. International Journal of Research in Marketing, 23(4), 373–390. https://doi.org/10.1016/j.ijresmar.2006.08.004
Kshetri, N. (2021). Artificial intelligence in supply chains: A conceptual framework. International Journal of Information Management, 56, 102271. https://doi.org/10.1016/j.ijinfomgt.2020.102271
Narendran, S., Jaiswal, R., Rai, P., Haralayya, B., Singh Yadav, A., & Kumar Mishra, A. (2024). Exploring the Impact of Emotional Intelligence on Leadership Effectiveness: A Meta-Analysis in Management Studies. Educational Administration: Theory and Practice, 30(4), 1668-1673.
O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group.
Organisation for Economic Co-operation and Development. (2019). Artificial intelligence and responsible business conduct. OECD. https://www.oecd.org
Porter, M. E., & Kramer, M. R. (2006). Strategy and society: The link between competitive advantage and corporate social responsibility. Harvard Business Review, 84(12), 78–92.
Porter, M. E., & Kramer, M. R. (2011). Creating shared value. Harvard Business Review, 89(1–2), 62–77.
Rai, P., Jaiswal, R., Singh, P., & Singh, A. K. (2024). Trends and Prospects for Artificial Intelligence in Business and Economics Research. International Journal of Innovations in Science, Engineering And Management, 341-344.
Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
United Nations. (2015). Transforming our world: The 2030 agenda for sustainable development. United Nations. https://sdgs.un.org/2030agenda
Yadav, A. S., Saxena, T., Vishwakarma, V., Kumar, A., & Tewari, R. (2025). The Role and Impact of AI in the Higher Education Organizations. African Journal of Biomedical Research, 28.
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