Getting Ahead of Bias in AI Applications
Learning goals in this first-year seminar articulate a range of skills needed to analyze the use of AI in healthcare, including biases and benefits.
Course Info:
- MED 22N "Getting Ahead of Bias in AI Applications"
- Winter 2025
- Instructor: Karleen Giannitrapani
Pedagogy:
In this first-year IntroSem, as students explore both potential benefits and serious concerns with generative AI in healthcare settings, course learning goals span a range of complexity. Students must develop clarity about foundational definitions related to AI in order to pose original questions and collect new evidence through interviews. Learning goals encompass multiple kinds of skills and knowledge needed to analyze AI's role in medicine.
While not addressed specifically in the learning goals, the course adopts an AI policy in the syllabus adapted from the CTL Syllabus Template for permitted AI uses under certain conditions, with the details of the policy discussed and developed with student input near the beginning of the term.
Course Learning Goals
Drawing on key theories from fields of qualitative research, ethics, and media critique, you will:
- Define artificial intelligence/intelligence, including characterizing why intelligence may be challenging to define
- Critically evaluate claims and discussion of AI present in popular media
- Understand the concept of “bias”
- Consider the concept of bias in Artificial Intelligence applications in healthcare
- Generate original research questions and identify / implement approaches for addressing those questions
- Describe why it is important to center the patient / caregiver / care team / provider / stakeholder needs
- Develop confidence to collect data from your own primary sources (do an interview or survey)