Role of Regulatory Sandboxes and MLOps for AI-Enabled Public Sector Services

Abstract

Abstract This paper discusses how innovations in public sector AI-based services must comply with the Artificial Intelligence Act (AI Act) regulatory frameworks while enabling experimentation and participation of diverse stakeholders throughout the Artificial Intelligence (AI) lifecycle . The paper examines the implications of the emerging regulation, AI regulatory sandboxes and Machine Learning Operations (MLOps) as tools that facilitate compliance while enabling co-learning and active participation of multiple stakeholders. We propose a framework that fosters experimentation with automation pipelines and continuous monitoring for the deployment of future public sector AI-based services in a regulatory-compliant and technically innovative manner. AI regulatory sandboxes can be beneficial as a space for contained experimentation that goes beyond regulatory considerations to specific experimentation with the implementation of ML frameworks. While the paper presents a framework based on emerging regulations, tools and practices pertaining to the responsible use of AI, this must be validated through pilot experimentation with public and private stakeholders and regulators in different areas of high-risk AI-based services.

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