2023 AI for Health Equity Symposium: Virtual Conference & Workshop Series

AIHES Conference

Course Description

Step into the world of Artificial Intelligence (AI) and Machine Learning (ML) where experts discuss timely and cutting-edge topics at the intersection of AI/ML and health equity. Experience the best of the 2-day conference and 4-week workshop series, all at your own pace. Dive deep into topics like responsible AI/ML practices, ethical assessment frameworks, bias identification, and data access for health equity research. Discover the intricate relationship between ethics, equity, and disparities in AI/ML applications. Gain practical skills in building ethical and equity-focused AI/ML models, from data harmonization to model interpretation. Prepare to revolutionize healthcare by unraveling the potential of AI/ML through this immersive, virtual learning experience. Enroll today to unlock a world of possibilities in AI for health equity.

Course Objectives

This course leverages the content recorded from the AIHES 2023 which covers the knowledge, skills, and critical thinking abilities to navigate the ethical and practical challenges in leveraging AI/ML for health equity, while fostering an understanding of the broader implications for ethics and equity in healthcare. The following are the critical learning objectives of this course:

  1. Gain a comprehensive understanding of the ethical considerations and responsible practices when applying AI/ML in healthcare for promoting health equity. 
  2. Develop the ability to assess and develop an ethical AI assessment framework that aligns with health equity goals.
  3. Explore the challenges, opportunities, and major data sources in AI/ML for health equity. 
  4. Acquire practical skills in AI/ML such as data access, sharing, harmonization, and curation, along with the ability to evaluate and analyze data to ensure its relevance and applicability to health equity research and interventions.
  5. Develop a deep understanding of the ethical dimensions and implications of AI/ML in healthcare, specifically regarding bias and its impact on equity and disparities. 

Course Content Experts

Newton Howard

Fay Cobb Payton

Gabriella Waters

Tracey Newman

Keolu Fox

Dakuo Wang

Saif Khairat

Ron Keesing

Alex John London

Reva Schwartz

Dr. Beverly Thompson

Muna Mohammed

Diego Mazzotti

Oreoluwa Akinyode

Solmaz Amiri

Chhavi Chauhan

Angela Thomas

Kolaleh Eskandanian

Sarah Riaz

Ilan Shapiro

Jay Patel

Shawn O'Neil

Charisse Madlock Brown

Jeffery McCullough

Miroslav Mihaylov

Alina Peluso

Jessica Lyons

Sara Stienecker

Aous Abdo

David Sanchez Carmona

Xiaoqian Jiang

Xia Ben Hu 

Yafen Liang

Na Zou

Kai Zhang

Felesia Stukes

Cheryl Brown

Course Structure

  • Module 1: Conference Day 1 (Recorded June 28, 2023)
    • Opening Keynote "The Future of The Brain", Newton Howard
    • Community Engagement Panel, Tracey Newman, Gabriella Waters, Keolu Fox
    • AI/ML Use Case, Dakuo Wang
    • Sources of Health Equity Data & Inequalities in Data, Saif Khairat
    • Responsible AI/ML, Ron Keesing
    • Developing an Ethical AI Assessment Framework, Alex John London, Reva Schwartz, Beverly Thompson
  • Module 2: Conference Day 2 (Recorded June 29, 2023)
    • Opening Keynote "Coding, Coded & Counting: A Bias Continuum", Fay Cobb Payton
    • Ethics in AI/ML, Chhavi Chauhan
    • Community Engagement Panel, Angela Thomas, Kolaleh Eskandanian, Sarah Riaz, Ilan Shapiro
    • What's on your mind? Audience Interaction
    • AI/ML Use Case, Jay Patel
    • Sources of Health Equity Data, Shawn O'Neil, Charisse Madlock Brown
  • Module 3: Workshop Series Week 1 "So You Want to do AI/ML Research for Health Equity? What you need to get started" (Recorded July 5-7, 2023)
    • Getting Started in AI/ML and Health Equity, Jeffery McCullough
    • Statistical and Machine Learning, Jeffery McCullough
    • Challenges & Opportunities in Applying AI/ML for Health Equity, Miroslav Mihaylov
  • Module 4: Workshop Series Week 2 "AI/ML for Health Equity Data Fundamentals" (Recorded July 10-14, 2023)
    • Major Data Sources Day 1, Alina Peluso
    • Major Data Sources Day 2, Alina Peluso
    • Data Access & Data Sharing, Jessica Lyons, Sara Stienecker
    • Data Harmonization and Curation, Aous Abdo
    • Evaluating Data, Aous Abdo
  • Module 5: Workshop Series Week 3 "Bias and Ethics" (Recorded July 17-21, 2023)
    • What is AI/ML Ethics?, Gabriella Waters
    • Why is it Important to Address Bias in AI/ML?, Gabriella Waters
    • Current State of AI/ML Ethics, David Sanchez Carmona
    • Tools for Identifying Bias in AI/ML, David Sanchez Carmona
    • How is it All Tied Together: The Relationship between Ethics, Equity, and Disparities, Xiaoqian Jiang, Xia Ben Hu, Yafen Liang, Na Zou, Kai Zhang, Felesia Stukes, Cheryl Brown
  • Module 6: Workshop Series Week 4 "Hands-on Session with a Use Case" (Recorded July 24-28, 2023)
    • Developing an AI/ML Health Equity Centric Research Question, Jay Patel
    • Building an Ethical and Equity Focused AI/ML Model, Jay Patel
    • Building & Interpreting the Model, Jay Patel
    • Dissemination, Jay Patel

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:

  • Newton Howard, PhD: Presenter
  • Gabriella Waters, PhD: Presenter
  • Tracey Newman: Presenter
  • Keolu Fox, PhD: Presenter
  • Dakuo Wang, PhD: Presenter
  • Saif Khairat, PhD: Presenter
  • Ron Keesing, PhD: Presenter
  • Alex John London, PhD: Presenter
  • Reva Schwartz: Presenter
  • Beverly Thompson, PhD: Presenter
  • Muna Mohammed: Presenter
  • Diego Mazzotti, PhD: Presenter
  • Oreoluwa Akinyode: Presenter
  • Solmaz Amiri, PhD: Presenter
  • Fay Cobb Payton, PhD: Presenter
  • Chhavi Chauhan, PhD: Presenter
  • Angela Thomas, PhD: Presenter
  • Kolaleh Eskandanian, PhD: Presenter
  • Sarah Riaz: Presenter
  • Ilan Shapiro, MD: Presenter
  • Jay Patel, PhD: Presenter
  • Shawn O'Neil, PhD: Presenter
  • Charisse Madlock Brown, PhD: Presenter
  • Jeffery McCullough, PhD: Presenter
  • Miroslav Mihaylov, PhD: Presenter
  • Alina Peluso, PhD: Presenter
  • Jessica Lyons: Presenter
  • Aous Abdo, PhD: Presenter
  • David Sanchez Carmona, PhD: Presenter
  • Xiaoqian Jiang, PhD: 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, Presenter
  • Tom Whitfield: AIHES Coordinator
  • Prabhjeet Singh, BDS, MSPH: AIHES23 Course Director
  • Alejandro Manrique-Pinell: Research Intern, AIHES23 Course Support
  • Khaled Abusumaya: Research Intern, AIHES23 Course Support
  • Michael Simms: AIHES Coordinator
  • Jojo Navarro, BFA: Central Hub Communications Team
  • Aidan Hoyal, MSIS, MA: Communications Hub, AIM-AHEAD
  • Paul Warhurst, PhD: Communications Hub, AIM-AHEAD
  • Zainab Latif, MCS: Communications Hub, AIM-AHEAD 


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.