MAPS AI 201: Effective and Trustworthy AI Adoption

Course Overview

Course Description

This interactive workshop is designed for public service professionals that already have a foundational knowledge of different kinds of AI technology (i.e. expert, predictive, generative, and agentic AI) and are ready to develop their capacity to assess and support trustworthy AI systems in their organizations. AI terminology is still evolving, so we will begin with a review of important terms. Next, we illustrate why it is important to assess AI systems using a socio-technical lens; effective risk identification and mitigation require holistic consideration of interactions between humans, AI technology, organizations, and their environments. We will then discuss key characteristics of trustworthy AI systems defined in the AI Risk Management Framework (AI RMF) developed by the National Institute of Standards and Technology (NIST). Throughout this discussion, workshop participants will practice applying a sociotechnical lens and the trustworthy AI characteristics to assess several public service AI use cases. Throughout the workshop, we will discuss approaches that support the trustworthiness of AI systems in practice. We will conclude with a participant-led discussion of next steps for supporting trustworthy AI in their organizations.

Intended Audience

This interactive workshop is designed for public service professionals that already have a foundational knowledge of different kinds of AI technology (i.e. expert, predictive, generative, and agentic AI) and are ready to develop their capacity to assess and support trustworthy AI systems in their organizations. 

Learning Outcomes

By the end of this course, you will be able to:

1. Practice taking a holistic approach to assessing AI systems by understanding how AI risks and issues arise from interactions between humans, AI technology, organizations, and their environments.
2. Deepen your  understanding of the characteristics of trustworthy AI systems defined in the AI RMF.
3. Apply a sociotechnical framework and the trustworthy AI characteristics defined in the AI RMF to analyze public service AI use cases.
4. Identify practices for supporting trustworthy AI systems in their organizations.

Course Materials

Course materials will be provided in class.

Instructors

Megan LePere-Schloop, PhD, MPA is a social scientist and professor in the John Glenn College of Public Affairs, where she researches and teaches public and nonprofit management. She received her PhD and MPA from the Department of Public Administration and Policy at the University of Georgia (UGA) and a BA in History from Oberlin College. Megan is a dedicated educator and was honored to receive the Ohio State Alumni Award for Distinguished Teaching, OSU’s highest honor for teaching, in 2020. Megan both uses computational methods, including AI, to conduct organizational research, and does research on AI. Prior to joining the OSU faculty, Megan’s professional experience spanned the public and nonprofit sectors.

Angie Westover-Muñoz
As Program Manager for the Program on Data and Governance, I collaborate closely with practitioners developing and implementing programs for the responsible use of artificial intelligence and advanced analytics. In this role, I aim to:

1. Connect practitioners with PDG’s researchers and, more broadly, with OSU’s robust community of researchers working on responsible data science (See TDAI RDS CoP).
2. Support PDG in convening the conversation around the issues and best practices for unlocking AI’s social and economic value while preventing undesired impacts.

Before joining Ohio State University, I was a program manager for the Technological Capabilities Division at CORFO (Chilean Agency for Economic Development). In this role, I had the opportunity to support numerous cross-sector collaborations for developing technology and studies to promote data-driven, informed policymaking.

My research focuses on the governance of technology and advanced analytics in public and private organizations. In particular, on the policies, management processes, and structures organizations use to ensure the responsible use of data and algorithmic systems for decision-making. More specifically, my doctoral dissertation develops a conceptual framework for public managers facing cross-sector data integration challenges in the context of smart city initiatives. This conceptual framework aims to aid managers in designing platforms for data integration for their city or region by providing them with different forms of governance and management structures that align with their smart city's different goals and priorities.

FAQ's

Follow these steps to make a cancellation:

1. Refer to your registration confirmation email
2. Scroll down to "Cancellation Policy"
3. Follow link in email
4. Enter the confirmation number from the registration email
5. At the top of the page, click on “Cancel Registration” button
6. Click Finish

Follow these steps to make a substitution: 

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5. At the top of the page, click on the “Substitute Registrant” button
6. Enter the name and email of the new registrant
7. Click Next
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Waitlist
What happens if I am on a waitlist for a course? You will receive an email confirming you have been added to a waitlist. If a spot becomes available, you will be notified by email.  

CEU and CPE Policy
Attendance in this course can earn:

• 0.6 Continuing Education Units (CEUs)
• 7.2 Continuing Professional Education Units (CPEs)

Attendance is recorded at check-in at the start of the day, and full CEU or CPE credit is awarded based on an honor system, with the expectation that participants are present and actively engaged for the full MAPS course.

Course Certificate
Your course certificate will be emailed to you following the course. It will reflect your Continuing Education Units (CEUs) and Continuing Professional Education Units (CPEs) earned and your attendance of the course. 

Photography
The Ohio State University may occasionally take pictures during educational events for use in program communication. If you do not want to be photographed during this course, please email the MAPS team at glenn-training@osu.edu.

MAPS Membership 
A MAPS member receives a discounted rate of $250 for each course with the initial purchase of at least 12 credits. Each credit equals one course for one individual.

To inquire about becoming a MAPS member, please fill out the MAPS Membership Form. A Glenn College Professional Development team member will reach out to you.

Questions or want to learn more?