AI Regulation Is (not) All You Need

Abstract

The development of processes and tools for ethical, trustworthy, and legal AI is only beginning. At the same time, legal requirements are emerging in various jurisdictions, following a deluge of ethical guidelines. It is therefore key to explore the necessary practices that must be adopted to ensure the quality of AI systems, mitigate their potential risks and enable legal compliance. Ensuring that the potential negative impacts of AI on individuals, society, and the environment are mitigated will depend on many factors, including the capacity to properly regulate its deployment and to mandate necessary internal best practices along lifecycles. Regulatory frameworks must evolve from abstract requirements to providing concrete operational mandates that enable better oversight mechanisms in the way AI systems operate, how they are developed, and how they are deployed. In view of the above, this paper explores the necessary practices that can be adopted throughout a comprehensive lifecycle audit as a key practice to ensure the quality of AI systems and enable the development of compliance mechanisms. It also discusses novel governance tools that enable bridging the current operational gaps. Such gaps were identified by interviewing experts, analysing adaptable tools and methodologies from the software engineering domain, and by exploring the state of the art of auditing. The results present recommendations for novel tools and oversight mechanisms for governing AI systems.

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