Ethics of Artificial Intelligence through a Cultural Anthropology Lens

For this episode, we are joined by Kadija Ferryman, an anthropologist who studies equity and policy in health risk prediction technologies. Dr. Ferryman is Faculty at the Berman Institute of Bioethics and Assistant Professor in the Department of Health Policy and Management at the Johns Hopkins Bloomberg School of Public Health.

Dr. Ferryman traces her path into studying technology through a cultural anthropology lens, beginning with an early curiosity about how different cultures define illness and disease. She explains how the cultural anthropology focus on beliefs, values, and power structures shapes the way she examines modern health technologies. Using examples like the sequencing of the human genome, she highlights how different scientific communities drew strikingly different conclusions from the same discovery, revealing deeper tensions about race, biology, and social meaning that continue to influence biomedical research.

Building on this foundation, Dr. Ferryman explores how bias becomes embedded in everyday health technologies, from pulse oximeters to clinical risk prediction algorithms. She describes how known inaccuracies of pulse oximeter readings for darker-skinned individuals persisted for decades and became especially visible during the COVID-19 pandemic. Extending these concerns to emerging areas like generative AI, she raises important questions about how biased data can shape both clinical care and healthcare systems more broadly. At the same time, she offers a more nuanced perspective: these flawed technologies can also serve as powerful windows into the inequities of our society and as opportunities to rethink how ethics is integrated into medicine and technological development.

Ferryman K, Mackintosh M, Ghassemi M. Considering Biased Data as Informative Artifacts in AI-Assisted Health Care. N Engl J Med. 2023 Aug 31;389(9):833-838.

Ethical Guidelines for AI:

https://healthaipartnership.org/health-equity-across-the-ai-lifecycle-heaal

https://www.chai.org/

https://nam.edu/our-work/programs/leadership-consortium/health-care-artificial-intelligence-code-of-conduct/

Select other publications by Dr. Ferryman:

Collins BX, Bélisle-Pipon JC, Evans BJ, Ferryman K, Jiang X, Nebeker C, Novak L, Roberts K, Were M, Yin Z, Ravitsky V, Coco J, Hendricks-Sturrup R, Williams I, Clayton EW, Malin BA; Bridge2AI Ethics and Trustworthy AI Working Group. Addressing ethical issues in healthcare artificial intelligence using a lifecycle-informed process. JAMIA Open. 2024 Nov 15;7(4):ooae108.

Shachar C, Drabo EF, Iwashyna TJ, Ferryman K. Addressing Racial and Ethnic Bias in Pulse Oximeters-A Wicked Problem. JAMA. 2025 Feb 18;333(7):563-564.

Ferryman K, Crews DC, Drabo EF, Iwashyna TJ, Kane O, Jackson JW. Adherence to FDA Guidance on Pulse Oximetry Testing Among Diverse Individuals, 1996-2024. JAMA. 2025 Feb 18;333(7):631-632

Also mentioned on the show: Joy Buolamwini Coded Bias