Course Description
In an era defined by transformative technological advances in healthcare, the integration of Artificial Intelligence (AI) and Machine Learning (ML) stands as a pivotal force in research. This short didactic course, guided by esteemed AI/ML experts, offers a vital exploration into the responsible application of these technologies without the need for prior coding experience. As we navigate the dynamic healthcare landscape, understanding and addressing the inherent biases within AI/ML systems become paramount.
Embark on a compelling journey where you'll unravel the intricacies of bias in AI/ML, from its subtle embedded nuances to proactive de-biasing methodologies. Empowered with this profound knowledge, you'll be equipped to implement tangible interventions in health equity research, ensuring a conscientious and ethical approach.
This timely course is a unique opportunity to delve into the ethical dimensions and strategies concerning the use of AI/ML in healthcare. With a laser focus on addressing disparities and fostering equitable care, participants will immerse themselves in responsible AI/ML practices, expert-guided de-biasing algorithms, and strategic approaches to identify and mitigate bias. Enroll now to seize the transformative potential of AI in advancing Health Equity.
Course Objectives
This course seeks to cover the key concepts, skills, and perspectives necessary for students to comprehend and apply AI and ML technologies ethically and effectively in pursuit of health equity:
- Understand the ethical considerations and challenges involved in leveraging AI and ML for health equity, and develop strategies to promote responsible AI practices in healthcare.
- Identify, analyze, and mitigate biases in AI/ML algorithms and models used in healthcare to address disparities and promote health equity.
- Evaluate the impact of biased AI/ML systems on healthcare outcomes, disparities, and access to equitable healthcare.
Course Content Experts
- Gabriella Waters, PhD
- Marzyeh Ghassemi, PhD
- David Sanchez Carmona
Course Structure
- Module 1: Responsible AI
- Module 2: The Current State of AI/ML Ethics
- Module 3: What does bias look like?
- Module 4: How do we identify bias? - Tools for identifying bias
- Module 5: How it’s all tied together: Ethics, Equity, & Disparities
Instrumental Persons
We would like to recognize and express our gratitude to the following people who shared in the creation and implementation of this course:
- Gabriella Waters, PhD: Presenter
- Marzyeh Ghassemi, PhD: Presenter
- David Sanchez Carmona: Presenter
- Nawar Shara, PhD: Co-Chair of AIHES 2023
- Usha Sambamoorthi, PhD: Co-Chair of AIHES 2023
- Toufeeq Ahmed Syed, PhD: Co-Chair of AIHES 2023
- Sara Stienecker, MS, MBA: AIHES Coordinator
- Tom Whitfield: AIHES Coordinator
- Michael Simms: AIHES Coordinator
- Prabhjeet Singh, BDS, MSPH: Course Director
- Jojo Navarro, BFA: Central Hub Communications Team
- Aidan Hoyal, MSIS, MA: Communications Hub, AIM-AHEAD
- Zainab Latif, MCS: Communications Hub, AIM-AHEAD
- Paul Warhurst, PhD: Communications Hub, AIM-AHEAD
Funding
The Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) program is funded by NIH, Agreement No.: 1OT2OD032581-01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.