An AI Convenor for Multi-Domain Co-Creation: A Governance-Constrained Decision-Rights and Escalation Framework

Authors

DOI:

https://doi.org/10.23726/cij.2026.1824

Keywords:

co-creation, Human-AI collaboration, governance, facilitation, decision rights, escalation, procedural fairness

Abstract

Artificial Intelligence (AI) increasingly summarises meetings and prompts participation, but multi-domain co-creation also requires rules for whose constraints count, when action must stop, and who may decide. We propose an AI Convenor as a governance-constrained facilitation role. The paper’s central artifact is a decision-rights and escalation framework that permits only pre-authorised procedural actions, defaults substantive choices to AI-propose/human-decide and accumulates trigger-specific human decisions and checkpoints. A short protocol and reusable records operationalise the framework. AIC-12 is offered as a candidate 12-item post-session set—not a validated instrument—alongside process indicators and conditional propositions for comparative testing. A synthetic vignette illustrates translation, option comparison, sensitive data, dissent, and closure. The contribution is a testable design pattern; no empirical effectiveness claim is made.

References

Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P. N., Inkpen, K., Teevan, J., Kikin-Gil, R., & Horvitz, E. (2019). Guidelines for human-AI interaction. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19) (Article 3, pp. 1–13). Association for Computing Machinery. https://doi.org/10.1145/3290605.3300233

Carlile, P. R. (2002). A pragmatic view of knowledge and boundaries: Boundary objects in new product development. Organization Science, 13(4), 442–455. https://doi.org/10.1287/orsc.13.4.442.2953

Colquitt, J. A. (2001). On the dimensionality of organizational justice: A construct validation of a measure. Journal of Applied Psychology, 86(3), 386–400. https://doi.org/10.1037/0021-9010.86.3.386

Diakopoulos, N. (2016). Accountability in algorithmic decision making. Communications of the ACM, 59(2), 56–62. https://doi.org/10.1145/2844110

Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114–126. https://doi.org/10.1037/xge0000033

Dietvorst, B. J., Simmons, J. P., & Massey, C. (2018). Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Management Science, 64(3), 1155–1170. https://doi.org/10.1287/mnsc.2016.2643

Drain, A., & Sanders, E. B.-N. (2019). A collaboration system model for planning and evaluating participatory design projects. International Journal of Design, 13(3), 39–52. https://www.ijdesign.org/index.php/IJDesign/article/view/3486

Edmondson, A. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350–383. https://doi.org/10.2307/2666999

Houtti, M., Zhou, M., Terveen, L., & Chancellor, S. (2025). Observe, ask, intervene: Designing AI agents for more inclusive meetings. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI '25) (Article 709, pp. 1–18). Association for Computing Machinery. https://doi.org/10.1145/3706598.3713838

Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174

Lee, M. K., Kusbit, D., Metsky, E., & Dabbish, L. (2015). Working with machines: The impact of algorithmic and data-driven management on human workers. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15) (pp. 1603–1612). Association for Computing Machinery. https://doi.org/10.1145/2702123.2702548

Lewis, K. (2003). Measuring transactive memory systems in the field: Scale development and validation. Journal of Applied Psychology, 88(4), 587–604. https://doi.org/10.1037/0021-9010.88.4.587

Logg, J. M., Minson, J. A., & Moore, D. A. (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, 151, 90–103. https://doi.org/10.1016/j.obhdp.2018.12.005

McAllister, D. J. (1995). Affect- and cognition-based trust as foundations for interpersonal cooperation in organizations. Academy of Management Journal, 38(1), 24–59. https://doi.org/10.5465/256727

Rayan, J., Kanetkar, D., Gong, Y., Yang, Y., Palani, S., Xia, H., & Dow, S. P. (2024). Exploring the potential for generative AI-based conversational cues for real-time collaborative ideation. In Proceedings of the 16th Conference on Creativity & Cognition (pp. 117–131). Association for Computing Machinery. https://doi.org/10.1145/3635636.3656184

Romme, A. G. L. (2023). Design science as experimental methodology in innovation and entrepreneurship research: A primer. CERN IdeaSquare Journal of Experimental Innovation, 7(2), 4–7. https://doi.org/10.23726/cij.2022.1427

Samrose, S., McDuff, D., Sim, R., Suh, J., Rowan, K., Hernandez, J., Rintel, S., Moynihan, K., & Czerwinski, M. (2021). MeetingCoach: An intelligent dashboard for supporting effective and inclusive meetings. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI '21) (Article 252, pp. 1–13). Association for Computing Machinery. https://doi.org/10.1145/3411764.3445615

Sanders, E. B.-N., & Stappers, P. J. (2008). Co-creation and the new landscapes of design. CoDesign, 4(1), 5–18. https://doi.org/10.1080/15710880701875068

Star, S. L., & Griesemer, J. R. (1989). Institutional ecology, translations, and boundary objects: Amateurs and professionals in Berkeley’s Museum of Vertebrate Zoology, 1907–39. Social Studies of Science, 19(3), 387–420. https://doi.org/10.1177/030631289019003001

von Busch, O., & Palmås, K. (2023). The corruption of co-design: Political and social conflicts in participatory design thinking. Routledge. https://doi.org/10.4324/9781003281443

Voorberg, W. H., Bekkers, V. J. J. M., & Tummers, L. G. (2015). A systematic review of co-creation and co-production: Embarking on the social innovation journey. Public Management Review, 17(9), 1333–1357. https://doi.org/10.1080/14719037.2014.930505

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Published

2026-09-16

How to Cite

Ochiai, M. (2026). An AI Convenor for Multi-Domain Co-Creation: A Governance-Constrained Decision-Rights and Escalation Framework. CERN IdeaSquare Journal of Experimental Innovation, 10(2), 144–150. https://doi.org/10.23726/cij.2026.1824

Issue

Vol. 10 No. 2 (2026): Special Issue on Co-Creation

Section

Part 3: Co-Creating with Machines

Categories

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Copyright (c) 2026 Masaya Ochiai

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