Ethical Decision Making
We are in need, now more than ever, of ethical leadership within the field of instructional technology. Many modern situations, such as the environmental impact of artificial intelligence (AI), development of more sophisticated cyber attacks, and technology’s impact on mental health to name a few, present distinct challenges regarding ethical decision-making for leaders.
Ethical decision-making (EDM) models provide leaders with a structured framework to center decision-making around as it pertains to ethical dilemmas within their individual field (Johnson et al., 2022). These models are often classified into three categories: theoretical/philosophical, practice-based, or specialty practice-based.
For example, when considering the use of AI within the telehealth field of medicine, leaders must balance the benefits, like patient personalization and digital data organization & analysis, with the limitations, such as the inclusion of false or generalized information in patient documentation & resources (Kumar & Sah, 2026).
An EDM model can be implemented within an organization, particularly a corporate or non-profit setting, as a strategic way to determine what should happen in a specific scenario. Based on the general taxonomy outlined by Johnson et al. (2026), a practice-based model could involve:
Stakeholder input: This determines everyone related to an organization who could be impacted by decision-making in some capacity.
Value alignment: This tests the decision against outlined organizational core values.
Multi-lens analysis: This reviews the dilemma through different questioning protocols (e.g. “How would this appear as a front-page headline?” Or “Does this decision positively impact more people than negative?”)
To illustrate this model, consider a scenario where a company is developing an AI tool to monitor productivity and increase company profits. The ethical issue here would be that while a tool could monitor employee productivity, its implementation may inadvertently track sensitive employee data. A specialty-based EDM model could be applied in this scenario. Shareholders want increased productivity and company profits, but employees want privacy and psychological safety. Using this model could lead to the development of stricter privacy guidelines that still allow employee productivity to be monitored but shields any potentially sensitive data, even if it reduces the efficacy of the AI monitoring tool.
References
Johnson, M. K., Weeks, S. N., Peacock, G. G., & Domenech Rodríguez, M. M. (2022).
Ethical decision-making models: a taxonomy of models and review of issues. Ethics
& Behavior, 32(3), 195–209. https://doi.org/10.1080/10508422.2021.1913593
Kumar, M., & Sah, R. (2026). AI Integration in Homoeopathic Science: Benefits, Risks, and Ethical Considerations. Homoeopathic Heritage, 52(1), 35–39.
