Placement: Stanford University and Joint Venture Silicon Valley
Mentors: Sara Singer, M.B.A., Ph.D., Professor of Medicine, Stanford University School of Medicine; Professor, Stanford Graduate School of Business (by courtesy); Professor, Freeman Spogli Institute for International Studies (by courtesy); Stanford Health Policy Associate
Quency Phillips, Executive Director, Lighthouse and Chief Equity Officer, Joint Venture Silicon Valley
Project: How Does Ethnicity Influence Public Perception of the Use of Artificial Intelligence (AI) in Health and Care?
Zainab Garba-Sani, M.Sc., is a 2022–23 U.K. Harkness Fellow in Health Care Policy and Practice. She is currently a clinical innovation manager at NHS England and NHS Improvement, where she is responsible for a range of programs that aim to transform health care through supporting the ideation, development, and adoption of innovation. In addition, she is cochair of the NHS Muslim Network, Partnerships Lead for TEDxNHS, and a volunteer with the Muslim Scouts Fellowship. Garba-Sani is a passionate advocate for equity and justice. During her undergraduate studies she became the first student ambassador for the international charity DKMS (We Delete Blood Cancer) and founded its first student society. In July 2018, she was honored with a Prime Minister’s Points of Light Award for her work in increasing the number of people of color registered as potential blood stem cell donors. Garba-Sani is also a patient advocate, working with charities, communities, health care professionals, industry, and policymakers to improve care for sickle cell disease. She is an alumna of the NHS Graduate Management Training Scheme and holds an M.Sc. in health policy from Imperial College London.
Project Overview: With the priority of tackling health inequities and the promise of digitization spanning all parts of the U.K. and U.S. health systems, artificial intelligence (AI) offers great potential to solve the complex challenges seen in both countries. Arguably, by increasing the diversity of those interacting with AI, health systems could help maximize its potential to reduce and minimize its risk to increase health inequities. Therefore, this research seeks to investigate the influences of ethnicity (and interaction effects with other demographic characteristics, such as social determinants of health) to understand the barriers and enablers of engaging with AI. Insights will be used to inform tailored policy solutions to enable people of color to benefit, rather than be disadvantaged by AI. This may include tailored campaigns which include culturally competent communications; specific research calls to address identified disparities; and the tailored development of algorithmic impact assessments.
This multimethod study intends to optimize multiple data sources and perspectives to produce a comprehensive and credible set of findings. In particular, 1) a survey (adapted from the researcher’s validated M.Sc. study), 2) focus groups of people from different racial backgrounds, and 3) a roundtable for data triangulation, in which the findings/ recommendations are reflected back to participants for validation. Additionally, this study intends to draw on a mixture of qualitative and quantitative data analysis and benefit from good practice/learnings from across and outside the health and care sector.