Ethical and Explainable GeoAI Workshop

About the Workshop

Ethical and Explainable GeoAI Workshop: Building Transparent, Fair, and Accountable GeoAI Systems

DATE & TIME: Thursday, February 27, 2025  | 8:30 AM – 4:00 PM

LOCATION: Chevron Studios 298-299, Zachry Engineering Education Complex, Texas A&M University

DESCRIPTION: The Ethical and Explainable GeoAI Workshop brings together students, researchers, practitioners, and policymakers to explore the development of AI-enhanced Geosystems that are transparent, fair, and accountable. This workshop delves into the ethical challenges and societal implications of deploying artificial intelligence in geospatial applications, emphasizing the need for equity, inclusivity, and transparency.  Participants will discuss cutting-edge methods for explainable AI, techniques for mitigating biases in geospatial data, and frameworks for ensuring accountability in GeoAI decision-making processes.  Through collaborative sessions and expert panels, the workshop aims to foster interdisciplinary dialogue and chart a path toward more trustworthy and responsible GeoAI technologies. Light Breakfast and Lunch will be served.

SPECIAL ISSUE: CONFIRMED

PARKING INFORMATION: There are three options for visitor parking at the Zachry Engineering Education Complex (pictured below): Polo Road Parking Garage, Northside Parking Garage, and Central Parking Garage. Each garage is pay-to-park by the hour. For more information on rates and how to pay once you arrive, visit the Visitor Parking website.

AGENDA:

Keynote Speakers

Dr. Goodchild is a pioneering expert in the field and the originator of the term “Volunteered Geographic Information” (VGI). His groundbreaking work has advanced spatial databases, data modeling, and the integration of GIS with other systems, fostering interdisciplinary collaboration across GIScience, urban science, and computer science. A member of the National Academy of Sciences and the American Academy of Arts and Sciences, Goodchild has received numerous honors, including the Prix Vautrin Lud and the Founder’s Medal from the Royal Geographical Society. With 598 peer-reviewed articles and 15 books to his name, he continues to shape the future of GIS. Goodchild is a Distinguished Lecturer at the Texas A&M Hagler Institute for Advanced Study.

Dr. Hashash specializes in urban tunneling, earthquake engineering, soil-structure interaction, and geotechnical applications of AI and visualization technologies. He co-developed DEEPSOIL, extensively used globally, and co-leads NIST’s investigation of the Champlain Towers South Collapse. His work shapes seismic design and engineering practices. Dr. Hashash is a Member of the National Academy of Engineering, a Fellow of the American Society of Civil Engineers (ASCE), a past president of the Geo-institute of ASCE and has received a number of teaching, university and professional awards including the Presidential Early Career Award for Scientists and Engineers and the ASCE 2014 Peck medal. Hashash is a Hagler Fellow at the Texas A&M Hagler Institute for Advanced Study.

Tentative Agenda

8:30 AM–9:00 AM	Opening Session (Check-In and Light Breakfast)
9:00 AM–10:00 AM	Keynote Speaker — Dr. Michael Goodchild
10:00 AM–10:50 AM	Foundations of Ethical and Explainable GeoAI: Philosophies, Frameworks, and Futures
11:00 AM–11:50 AM	Innovative Pathways: Methods and Algorithms for Transparent and Fair GeoAI
12:00 PM–1:00 PM	Networking Lunch
1:00 PM–2:00 PM	Keynote Speaker — Dr. Youssef Hashash
2:00 PM–2:50 PM	From Lab to Landscape: Real-World Applications and Accountability in GeoAI
3:00 PM–3:50 PM	End Session

Ethical and Explainable GeoAI Description

The Ethical and Explainable GeoAI Workshop is based on the principle that machine learning systems are not merely predictive models but tools that profoundly influence real-world decision-making. The transition from data collection and model prediction to decision automation highlights the need for robust ethical evaluations, especially when system outcomes affect diverse populations.

Incorporating societal values into machine learning—such as considering cost sensitivity when errors have substantial impacts—moves beyond accuracy as the sole measure of success. In sensitive scenarios, threshold-setting adjustments are essential to align AI decision-making with ethical standards, as in predictive policing, where missteps can reinforce biases or initiate feedback loops. These “runaway feedback loops,” where model predictions influence future training data, are a key issue that the workshop will address by promoting transparent, ethically sound model designs.

By tackling these pressing ethical challenges, Texas A&M will help cultivate AI systems that are accountable, equitable, and transparent. This workshop not only supports Texas A&M’s commitment to responsible AI but also creates an interdisciplinary platform for collaboratively navigating these ethical complexities in machine learning.

Workshop Co-Chairs

Organization Committee Members

• Sulong Zhou (Co-Chairs), Texas A&M University
• Xinyue Ye (Co-Chairs), Texas A&M University
• Martin Peterson (Co-Chairs), Texas A&M University
• James Doss-Gollin, Rice University
• Daniel Goldberg, Texas A&M University
• Junfeng Jiao, UT Austin
• Gengchen Mai, UT Austin
• Jinmeng Rao, Google DeepMind
• Cason Schmit, Texas A&M University
• Yunpeng (Jack) Zhang, University of Houston
• Zhe Zhang, Texas A&M University
• Lei Zou, Texas A&M University