TrustworthinessEthicsAccountabilitycs.CE
Report of the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science
Lois Curfman McInnes, Dorian Arnold, Prasanna Balaprakash, Mike Bernhardt, Beth Cerny, Anshu Dubey, Roscoe Giles, Denice Ward Hood, Mary Ann Leung, Vanessa Lopez-Marrero, Paul Messina, Olivia B. Newton, Chris Oehmen, Stefan M. Wild, Jim Willenbring, Lou Woodley, Tony Baylis, David E. Bernholdt, Chris Camano, Johannah Cohoon, Charles Ferenbaugh, Stephen M. Fiore, Sandra Gesing, Diego Gomez-Zara, James Howison, Tanzima Islam, David Kepczynski, Charles Lively, Harshitha Menon, Bronson Messer, Marieme Ngom, Umesh Paliath, Michael E. Papka, Irene Qualters, Elaine M. Raybourn, Katherine Riley, Paulina Rodriguez, Damian Rouson, Michelle Schwalbe, Sudip K. Seal, Ozge Surer, Valerie Taylor, Lingfei Wu
October 3, 2025
arXiv.org
4 citations
Key Takeaways
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AI should enhance rather than replace human scientific judgment: The workshop emphasizes that AI systems must be integrated into scientific workflows in ways that "preserve human creativity, trust, and scientific rigor," indicating that responsible AI deployment in science requires maintaining human oversight and decision-making authority rather than automating scientific conclusions.
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Building trustworthy AI requires intentional design of both social and technical systems: The report advocates for "socio-technical co-design" that treats social factors (collaboration models, training, workforce development) and technical factors (software architecture, AI systems) as equally important interdependent parts, suggesting that responsible AI cannot be achieved through technical solutions alone.
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Responsible AI guidelines are an urgent priority: The workshop identified "responsible AI guidelines" as a near-term pilot project priority alongside infrastructure development, signaling that the scientific computing community recognizes the need for concrete ethical frameworks to govern AI use in research before widespread deployment occurs.
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Workforce development must keep pace with AI advancement: The report calls for "innovative training pipelines that keep pace with rapid technological change," acknowledging that responsible AI adoption requires ensuring scientists and engineers understand both the capabilities and limitations of AI systems they use.
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Cross-disciplinary collaboration is essential for responsible AI in science: By convening experts from national labs, academia, industry, and community organizations, the workshop recognized that responsible scientific AI ecosystems require diverse perspectives beyond computer scientists and engineers.