AI Ethics Health Equity

3 Credits · Level: Undergraduate

Artificial intelligence is transforming health and healthcare at an unprecedented pace—reshaping diagnosis, treatment, research, and patient care. These developments bring both ethical promise and peril: opportunities for more personalized, efficient, and equitable care, alongside risks of algorithmic bias, data exploitation, and the reinforcement of existing health inequities. The stakes are especially high for patient populations who are, in virtue of their social identity, health condition, and/or geopolitical status already highly vulnerable. This advanced undergraduate course explores the ethical challenges of AI in healthcare with a focus on vulnerable and marginalized populations. Drawing on bioethics, political philosophy, critical race theory, feminist ethics, and disability studies, students will examine how AI systems can both mitigate and exacerbate patterns of domination and exclusion in global health contexts. The course is organized into 4 main units: 1. AI and Racial Minorites This unit will explore how AI systems can both reproduce and amplify racial disparities in healthcare as well as identify and mitigate racial bias in healthcare. Topics will include racial bias in diagnostic systems, the use of race-based corrections in medical data, and the historical roots of algorithmic discrimination. Students will consider frameworks from critical race theory and public health ethics to analyze case studies where AI has both harmed and helped communities of color. 2. AI and Gender and Sexuality Minorities This section interrogates how gendered and heteronormative biases shape AI systems in health contexts. Topics include algorithmic discrimination in reproductive health, digital surveillance of trans and nonbinary patients, and the use of AI in gender-affirming care. Readings from feminist ethics and queer theory will guide discussions on embodiment, privacy, and the politics of data representation. 3. AI and Disabled Patients Focusing on the intersection of AI and disability, this section explores the dual role of technology as both a tool of empowerment and a potential source of exclusion. Students will study ethical questions surrounding autonomy, consent, and accessibility in AI-assisted care—from prosthetics and mobility aids to AI-driven mental health tools. The section draws on disability studies to critique how ableist assumptions can be encoded into data, and the design and deployment of AI systems. 4. AI and the Global South The final unit situates AI within global health and development contexts. Students will examine how AI-driven health interventions intersect with global inequalities, focusing on issues of data colonialism, resource distribution, and the ethics of technological dependency. Case studies from the Global South will illuminate both the potential of AI to extend healthcare access and the risks of deepening digital divides. By the end of the course, students will be equipped to critically assess AI’s ethical landscape in health contexts, identify sources of injustice and bias in technological systems, and articulate frameworks for developing more equitable and inclusive AI practices in healthcare.