Session Lead: Alexandra Fries (University of Maryland Center for Environmental Science)
Co-Lead(s):
Session Format: Oral presentations
Session Description:
Artificial Intelligence (AI) and machine learning are rapidly transforming how we observe, model, and manage complex environmental systems. Within the Chesapeake Bay and beyond, AI applications now extend from data collection to ecosystem forecasting to decision-support tools for restoration and resilience. Yet, as these technologies become more integral to environmental research, the full potential of AI can only be realized through transdisciplinary collaboration—bringing together computer scientists, ecologists, social scientists, data managers, and community stakeholders to ensure that tools are not only powerful but also usable, ethical, and aligned with management needs and goals.
Co-designing and co-developing research projects across disciplines leads to more effective outcomes and better use of results. As AI tools are incorporated into research methods and projects, this is becoming increasingly important. This session will explore how team science principles and transdisciplinary practices can be incorporated into AI-based environmental research to enhance innovation, communication, and impact. While AI excels at pattern recognition and prediction, it requires contextual grounding—ecological understanding, stakeholder input, and social insight—to guide meaningful application. Talks will highlight real-world examples where collaborative frameworks improved the design, interpretation, and application of AI systems for water quality modeling, habitat restoration, and climate adaptation planning.
The session will distill lessons from research projects using AI tools—ranging from hybrid ecological modeling teams to cross-sector AI ethics initiatives. It will also identify barriers such as disciplinary silos, data incompatibility, and uneven access to computing resources, and propose strategies to overcome them through collaborative infrastructure, shared language, and inclusive governance.