Making a Case for Research Collaboration Between Artificial Intelligence and Operations Research Experts

Trustworthy AI requires interdisciplinary expertise: The workshops emphasized that developing trustworthy AI systems benefits from combining AI researchers with Operations Research experts, suggesting that responsible AI development cannot be achieved by a single discipline alone.

Cultural differences between fields hinder collaboration: The paper identifies that overcoming "cultural differences" between AI and OR communities is a significant barrier to collaboration, indicating that responsible AI implementation requires bridging different professional perspectives and methodologies.

Five concrete mechanisms can accelerate responsible AI development: The report recommends specific structural changes—including joint funding opportunities, integrated education programs, long-term research initiatives, aligned conferences/journals, and shared benchmarks—to institutionalize AI/OR collaboration and improve decision-making systems.

Real-world "Challenge Problems" should drive research priorities: The workshops used specific challenge problems to guide collaboration, suggesting that responsible AI research should be grounded in practical applications rather than purely theoretical pursuits.

Societal impact is a central motivation: The workshops explicitly aimed to "maximize societal impact," indicating that the AI and OR communities recognize their responsibility to ensure AI systems serve public benefit when properly integrated with optimization and decision-science expertise.