26th Annual Colleagues in Jesuit Business Education Meeting

Experience level: 
Intended Audience: 
Vivek Patil, Nicholas J.C. Santos, Chandrasekhar Valluri

Enabling students to make moral and ethical data-driven decisions

The use of analytical tools such as Machine Learning (ML) and artificial intelligence (AI) for data driven decision making is gaining popularity in business. As a result, many Business Schools, including those situated within Jesuit Universities, are equipping their students with the necessary analytical skills to prepare them for the Business world. We contend that the blind reliance on the results of these tools could lead to the exploitation of vulnerable populations in the community. In their examination of a Credit Union that extended auto loans to subprime borrowers, Valluri, Raju, and Patil (2021) found that the recommendations of Machine Learning models to the Credit Union to manage customer churn were predatory in nature. For example, one recommendation of the models was to target subprime borrowers in rural areas with high unemployment, specifically those with really low FICO scores and high loan to value ratios. Although this would result in better churn management, the suggested marketing tactics from the study were morally questionable and against the mission founded philosophy of the credit union. In this paper, we utilize the tenets of the Integrative Justice Model (IJM, Santos and Laczniak, 2009) and Asset Based Community Development (ABCD, Kretzmann and McKnight 1993) model to propose how the Credit Union that is discussed in Valluri et al. (2021) could not only manage their customer churn problem, but also live their mission of serving the financially vulnerable subprime borrowers. Our paper recommends that the Credit Union serve as a community asset in accordance with the ABCD model and work with other assets such as community members and additional institutions and associations for the overall betterment of the subprime borrowers. We view the IJM as a process framework that the Credit Union could utilize to engage with different assets in a mutually beneficial manner that results in a positive social impact. The lessons from our work would suggest that Jesuit Business Schools should not only focus on equipping our students with the analytical skills that can help students make data-driven decisions, but also equip them with frameworks like the IJM and the ABCD models that encourage them to place the results of analytical models in a moral and ethical context.