SPECIAL SESSION #02

Jess J. Montgomery

Colorado Technical University

• jessicajomontgomery@gmail.com

Artificial intelligence increasingly connects animal measurements to decisions about diagnosis, treatment, behavior, welfare, and management. Reliable predictions alone do not establish when a recommendation should guide action, who may challenge it, or what should happen when its supporting evidence changes.

This session examines decision authority, professional judgment, and assurance in AI-supported veterinary and animal-welfare systems. It welcomes contributions spanning clinical practice, companion animals, livestock, wildlife, shelters, and sanctuaries, including avian and exotic species for which data may be sparse or heterogeneous. Relevant technologies include sensing and monitoring systems, predictive models, and decision-support tools that translate measurements into consequential recommendations.

Contributions may address measurement quality, validation across species and settings, uncertainty communication, human oversight, and the traceability of evidence and decisions. Particular attention is given to how professionals contest, override, revise, or withdraw AI recommendations, and how systems verify that these interventions take effect when evidence or circumstances change.

The session invites empirical studies, technical methods, case analyses, and evaluations of deployed or emerging systems. It will bring researchers, veterinarians, animal-welfare practitioners, and engineers together to identify testable assurance requirements and approaches that preserve accountable professional judgment while enabling useful AI applications across diverse animal-care settings and informing the design of future systems.

Topics of interest include:

• AI-assisted clinical and animal-welfare decision support;
• Measurement quality and evidence validity in AI systems;
• Validation across species, populations, and care settings;
• Uncertainty communication and limits of recommendations;
• Professional judgment and human–AI interaction;
• Decision authority, escalation, and override mechanisms;
• Evidence provenance and decision traceability;
• Reassessment and withdrawal of outdated recommendations;
• Verification of interventions and downstream effects;
• Safety monitoring and accountable deployment in animal care.

Jess J. Montgomery is an AI governance executive, practitioner, and doctoral researcher at Colorado Technical University, where she is pursuing a Doctor of Computer Science in AI and Big Data Analytics. Her research examines AI-assisted decision support in companion-bird care, with a focus on changing evidence, professional judgment, decision authority, and whether governance interventions produce verifiable changes in operational systems.
Her experience spans enterprise cloud modernization, DevSecOps, veterinary operations, and animal-welfare leadership. She previously served as executive director of Louisville Metro Animal Services and held a veterinary and sanctuary leadership role in Qatar involving 300+ animals. She is a U.S. Army Veterinary Corps veteran with an MBA, an MS in Artificial Intelligence and Machine Learning, and degrees in veterinary technology.
Ms. Montgomery writes and speaks on AI governance, accountability, and operational readiness. Her work connects technical assurance questions with the practical responsibilities of professionals making consequential decisions about animal health and welfare.