Advancing Ethical Considerations for Data Science in Injury and Violence Prevention.

Abstract

Data science is an emerging field that provides new analytical methods. It incorporates novel data sources (eg, internet data) and methods (eg, machine learning) that offer valuable and timely insights into public health issues, including injury and violence prevention. The objective of this research was to describe ethical considerations for public health data scientists conducting injury and violence prevention-related data science projects to prevent unintended ethical, legal, and social consequences, such as loss of privacy or loss of public trust. We first reviewed foundational bioethics and public health ethics literature to identify key ethical concepts relevant to public health data science. After identifying these ethics concepts, we held a series of discussions to organize them under broad ethical domains. Within each domain, we examined relevant ethics concepts from our review of the primary literature. Lastly, we developed questions for each ethical domain to facilitate the early conceptualization stage of the ethical analysis of injury and violence prevention projects. We identified 4 ethical domains: privacy, responsible stewardship, justice as fairness, and inclusivity and engagement. We determined that each domain carries equal weight, with no consideration bearing more importance than the others. Examples of ethical considerations are clearly identifying project goals, determining whether people included in projects are at risk of reidentification through external sources or linkages, and evaluating and minimizing the potential for bias in data sources used. As data science methodologies are incorporated into public health research to work toward reducing the effect of injury and violence on individuals, families, and communities in the United States, we recommend that relevant ethical issues be identified, considered, and addressed.