It Takes a Village: Participation, Data, and Ethics in Health AI

New artificial intelligence (AI) tools will shift the paradigm of healthcare and redefine how triage, diagnosis, and treatment are performed. This thesis examines studies analyzing ethical and practical issues of developing health AI tools and some suggested solutions, such as changes in data collection and study design. Though the dangers of AI of which we are aware are currently well-described, none of them have a simple solution. After an extensive narrative literature review of AI scholarship, I present common issues discussed in the literature and propose some solutions gleaned from them. One such solution is participatory design methods. Participatory design methods can guide the development of more ethical AI tools by involving the communities they affect from the beginning of the project. If participatory methods were consistently integrated into clinical trials, they could help resolve problems such as disconnects between factions of multidisciplinary research groups, patient data concerns about overbroad and irrelevant data collection, and even racial bias from data sources and uneven/unrepresentative data collection. The integration of participatory methods in clinical trials would lead to better and more ethical first-generation AI tools, which is essential because the data from these tools will influence those created in the future. This improvement would, in turn, lead to better AI tools in the future by improving and equalizing their performance across more treatment groups, as well as helping to make health AI more useful to patients and physicians.Polymathic Scholar