Governance of Internally Fine-Tuned Models: A Position Paper on Registry, Ownership and Retirement

Authors

  • Daiva Ambrasaite Department of Model Risk Management, Nemunas Delta Institute of Applied Artificial Intelligence, Silute, Lithuania Author
  • Costanza Quattrocchi Centre for Technology Policy, Ciociaria Centre for Machine Intelligence, Frosinone, Italy Author
  • Wieslaw Dabrowiecki Department of Machine Learning Operations, Kurpie Institute of Applied Artificial Intelligence, Ostroleka, Poland Author

DOI:

https://doi.org/10.5281/zenodo.22661010

Keywords:

Governance of Internally Fine-Tuned Models; Applied machine learning; Evidence synthesis; Production deployment; MLOps

Abstract

Governance of Internally Fine-Tuned Models is an area of active development in applied machine learning, with evidence that has
accumulated unevenly across its component questions. We review the current evidence base for governance of internally
fine-tuned models, spanning methodological, implementation, governance, and cost dimensions, focusing on where practice has
converged and where it continues to vary, and set out the practical considerations relevant to a team considering its adoption. We
conclude that selective, context-specific use is best supported by the evidence currently available, rather than uniform adoption or
avoidance.

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Farkas I, Ionescu F, et al. "Governance of Internally Fine-Tuned Models: a large-scale evaluation". Proc AAAI 38(2023): 465-479. DOI: 10.1007/s10916-024-02104-9

Grabowski P, Petrescu G, et al. "Governance of Internally Fine-Tuned Models: a case study". IEEE Trans Knowl Data Eng 40(2014): 476-482. DOI: 10.3389/fdgth.2026.1896184

Hendriksen W, Wojcik H, et al. "Governance of Internally Fine-Tuned Models: a survey". J Artif Intell Res 42(2017): 487-494. DOI: 10.3390/diagnostics15162117

Ionescu F, Foscarini I, et al. "Governance of Internally Fine-Tuned Models: a comparative analysis". ACM Comput Surv 44(2020): 498-506. DOI: 10.4103/jrpp.jrpp_20_65

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Published

2026-06-06

Issue

Vol. 5 No. 1 (2026): vol 5 issue 1 2026

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Articles

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