Defense Date


Graduation Date

Spring 5-13-2022


One-year Embargo

Submission Type


Degree Name



Health Care Ethics


McAnulty College and Graduate School of Liberal Arts

Committee Chair

Gerard Magill

Committee Member

Joris Gielen

Committee Member

Peter Osuji


artificial intelligence, machine learning, ethical governance, data governance, electronic health records, precision medicine, biotechnology, ai ethics, ethical implications


With the Internet Age and technology progressively advancing every year, the usage of Artificial Intelligence (AI) along with Machine Learning (ML) algorithms has only increased since its introduction to society. Specifically, in the healthcare field, AI/ML has proven to its end-users how beneficial its assistance has been. However, despite its effectiveness and efficiencies, AI/ML has also been under scrutiny due to its unethical outcomes. As a result of this, two polarizing views are typically debated when discussing AI/ML. One side believes that AI/ML usage should continue regardless of its unsureness, while the other side argues that this technology is too dangerous and should not be utilized at all. Given the fact that AI/ML can provide prompt and fairly accurate results, it is unrealistic to assume that AI/ML usage will end any time soon. Therefore, governance of AI/ML is needed to ensure that these technologies are reliable.

Notably, AI governance has been positively reviewed and pushed for by scholars in the field. While AI governance does guarantee a sense of oversight on AI/ML, this form of governance is not sustainable. AI governance primarily focuses on the safety of the technology, with ethical, legal, and social factors serving as elements of AI governance. The safety of AI/ML is only one of the considerations for producing and ensuring ethical AI/ML. Ethical governance of AI/ML, which concentrates on incorporating ethics into all aspects of AI/ML—specifically, narrowing in on the stakeholders involved, will lead to not only a safer product but a more viable one as well. Thus, ethical governance of AI/ML must be advocated for in order to bring more awareness, which would lead to greater research and implementation of this type of governance.

Although AI/ML can be used for a multitude of areas, the healthcare industry is slightly more significant, especially since these technologies directly affect the patients’ health. This dissertation explores the contribution of ethical governance of AI/ML in several facets of healthcare. As AI/ML requires big data to provide outcomes, the context of data analytics is discussed. Other areas the dissertation explores are clinical decision-making, end-of-life decisions, and biotechnology. While these topics certainly do not cover the whole healthcare field, the dissertation attempts to include a wide range of AI/ML functions from the beginning of its process (with data analytics) to the future of AI/ML (with biotechnology). With each of these areas of interest, various ethical governance principles are introduced and endorsed for to develop ethical AI/ML. The goal of this dissertation in discussing the contribution of ethical governance of AI/ML in healthcare is to provide a foundational groundwork for more future research of the ethical governance of AI/ML.