Governance-Driven Development: Embedding Regulatory Requirements in AI Development Workflows

Simon Jarvers, Orestis Papakyriakopoulos
Proceedings of Fifth European Conference on Algorithmic Fairness, PMLR 350:365-371, 2026.

Abstract

AI governance under the EU AI Act faces a structural misallocation of effort: management-based regulation, combined with the credence-good properties of governance outcomes, pushes resource-constrained organisations toward verification-facing documentation rather than the implementation it is meant to verify. We propose Governance-Driven Development (GDD), a framework that treats AI Act obligations and the requirements of supporting standards for safe AI development as formal specifications for software development work. GDD runs on two nested action cycles. A planning cycle scopes obligations against organisational context to produce specifications. An implementation cycle converts each specification into the artefact it specifies, with traceability back to the regulatory source. AI agents perform template-driven work; humans retain interpretation and accountability. The framework is in early implementation at an AI Startup, with unresolved load-bearing questions around knowledge base architecture and obligation decomposition.

Cite this Paper

BibTeX
@InProceedings{pmlr-v350-jarvers26a, title = {Governance-Driven Development: Embedding Regulatory Requirements in AI Development Workflows}, author = {Jarvers, Simon and Papakyriakopoulos, Orestis}, booktitle = {Proceedings of Fifth European Conference on Algorithmic Fairness}, pages = {365--371}, year = {2026}, editor = {De Bie, Tijl and Defrance, MaryBeth and Calders, Toon and Nguyen, Dennis}, volume = {350}, series = {Proceedings of Machine Learning Research}, month = {02--04 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v350/main/assets/jarvers26a/jarvers26a.pdf}, url = {https://proceedings.mlr.press/v350/jarvers26a.html}, abstract = {AI governance under the EU AI Act faces a structural misallocation of effort: management-based regulation, combined with the credence-good properties of governance outcomes, pushes resource-constrained organisations toward verification-facing documentation rather than the implementation it is meant to verify. We propose Governance-Driven Development (GDD), a framework that treats AI Act obligations and the requirements of supporting standards for safe AI development as formal specifications for software development work. GDD runs on two nested action cycles. A planning cycle scopes obligations against organisational context to produce specifications. An implementation cycle converts each specification into the artefact it specifies, with traceability back to the regulatory source. AI agents perform template-driven work; humans retain interpretation and accountability. The framework is in early implementation at an AI Startup, with unresolved load-bearing questions around knowledge base architecture and obligation decomposition.} }
Endnote
%0 Conference Paper %T Governance-Driven Development: Embedding Regulatory Requirements in AI Development Workflows %A Simon Jarvers %A Orestis Papakyriakopoulos %B Proceedings of Fifth European Conference on Algorithmic Fairness %C Proceedings of Machine Learning Research %D 2026 %E Tijl De Bie %E MaryBeth Defrance %E Toon Calders %E Dennis Nguyen %F pmlr-v350-jarvers26a %I PMLR %P 365--371 %U https://proceedings.mlr.press/v350/jarvers26a.html %V 350 %X AI governance under the EU AI Act faces a structural misallocation of effort: management-based regulation, combined with the credence-good properties of governance outcomes, pushes resource-constrained organisations toward verification-facing documentation rather than the implementation it is meant to verify. We propose Governance-Driven Development (GDD), a framework that treats AI Act obligations and the requirements of supporting standards for safe AI development as formal specifications for software development work. GDD runs on two nested action cycles. A planning cycle scopes obligations against organisational context to produce specifications. An implementation cycle converts each specification into the artefact it specifies, with traceability back to the regulatory source. AI agents perform template-driven work; humans retain interpretation and accountability. The framework is in early implementation at an AI Startup, with unresolved load-bearing questions around knowledge base architecture and obligation decomposition.
APA
Jarvers, S. & Papakyriakopoulos, O.. (2026). Governance-Driven Development: Embedding Regulatory Requirements in AI Development Workflows. Proceedings of Fifth European Conference on Algorithmic Fairness, in Proceedings of Machine Learning Research 350:365-371 Available from https://proceedings.mlr.press/v350/jarvers26a.html.

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