The field of Artificial Intelligence (AI) has garnered considerable attention in recent times. Organizations are making very large investments to increase their computational capacity in order to train AI models to take advantage of improvements in machine learning technologies. There is every reason to be optimistic about the potential of AI technology to improve outcomes in important areas such as medicine and education, while also providing potential for economic growth to improve living standards. Generative Artificial Intelligence (GenAI), which is a form of Artificial Intelligence, is a rapidly emerging technology used to generate seemingly intelligent responses to text-based prompts (Peres et al. 2023). GenAI technologies are more accessible to non-specialists due to the availability of user-friendly interfaces and cloud-based services. This broader access has sparked innovation and creativity across a number of sectors, allowing individuals and smaller organisations to leverage GenAI for a variety of applications (Ferrara 2024).
GenAI focuses on the production of new content, which implies that it can accomplish tasks that have been considered to-date to require human capabilities. Unlike traditional AI, which is focused on recognizing patterns or making predictions, GenAI actively creates novel results, which involves complex algorithms and models that learn from large data sets, identify underlying structures, and reproduce them in new ways (Ferrara 2024). While GenAI has a high potential to do good, there are also justifiable concerns, which include social challenges (e.g., job loss) and privacy issues (e.g., revealing and misuse of sensitive information) (Grewal et al. 2024; Ferrara 2024). GenAI presents three major challenges that society will surely face in the future and that must be considered in the present: “(1) preserving and growing human capacity; (2) protecting social belonging and human connection; and (3) ensuring the equitable distribution of the benefits of AI” (Grewal et al. 2024, p. 871). Jesuit business education must consider these three challenges as they pertain to recognizing the value of human beings and human intelligence.
The necessary foundation of GenAI is data, which is intertwined with society. People generate data and it is people who must decide how data should be collected, classified and used. To make these decisions, it is important to make a distinction between AI and human intelligence. Human intelligence pertains to a person in their entirety, whereas AI understands intelligence functionally and assumes that the function of the human mind can be broken down into digitized steps that machines can perform completely (Catholic Church 2025, §10). Important social challenges presented by GenAI are that applications that do not consider the full scope of a person’s being have the potential to threaten the sanctity of life and the dignity of the human person. Specific concerns include the fact that by taking over mundane tasks, GenAI frequently forces workers to adapt to the speed and demands of machines rather than machines being designed to support those who work, which has the negative effects of reducing workers skills, subjecting them to surveillance, and relegating them to rigid and repetitive tasks (Catholic Church 2025, §67). This runs the risk of increasing social inequality and reducing perceived human value. To address these issues, future business leaders need to employ an ethical framework to ensure that they make appropriate decisions when using GenAI.
The value of utilizing an ethical framework is that in addition to addressing GenAI’s negative impacts on society, positive impacts of GenAI on society can be encouraged. This research uses a normative ethical model known as the Integrative Justice Model (IJM) to support business decisions regarding the use of GenAI. The IJM includes five key elements of fair and just business interactions with impoverished populations (Laczniak and Santos 2011; Santos and Laczniak 2009). These elements are authentic engagement with non-exploitive intent; value co-creation; investment in future consumption; stakeholder interest representation, and long-term profit management. A contribution of this research is an illustration of how each of these five elements can be utilized to improve positive impacts of GenAI on society.
As Jesuit business schools embrace GenAI it is imperative that they do so recognizing the value of human beings and the significance of moral decision making. In this paper we have drawn on the Catholic church document Antiqua et Nova as well as a normative ethical framework labeled the Integrative Justice Model. This research illustrates how these two frameworks can inform our approach to using GenAI in Jesuit Business Schools in order to have a positive impact on society.
Appendix: References
Catholic Church (2025). Antiqua Et Nova: Note on the Relationship Between Artificial Intelligence and Human Intelligence. Retrieved February 26, 2025 from https://www.vatican.va/roman_curia/congregations/cfaith/documents/rc_ddf_doc_20250128_antiqua-et-nova_en.html
Ferrara, E. (2024). GenAI against humanity: Nefarious applications of generative artificial intelligence and large language models. Journal of Computational Social Science, 1-21.
Grewal, D., Guha, A. and Becker, M. (2024). AI is Changing the World: For Better or for Worse? Journal of Macromarketing, 44(4), 870-882.
Hagerty, A. and Rubinov, I. (2019). Global AI ethics: a review of the social impacts and ethical implications of artificial intelligence. arXiv preprint arXiv:1907.07892.
Laczniak, G.R. and Santos, N.J.C. (2011). The Integrative Justice Model for Marketing to the Poor: An Extension of S-D Logic to Distributive Justice and Macromarketing. Journal of Macromarketing, 31(2), 135-147.
Peres, R., Schreier, M., Schweidel, D. and Sorescu, A. (2023). On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice. International Journal of Research in Marketing, 40(2), 269-275.
Puntoni, S. (2024). Artificial Intelligence in Marketing: From Computer Science to Social Science. Journal of Macromarketing, 44(4), 883-885.
Santos, N.J.C. and Laczniak, G.R. (2009). Marketing to the Poor: An Integrative Justice Model for Engaging Impoverished Market Segments. Journal of Public Policy & Marketing, 28(1), 3-15.