AI Gatekeepers: Defining Core AI Concepts for Effective Regulation

The rapid integration of Artificial Intelligence into commercial and private sectors has led to a new class of intermediaries, dubbed "AI gatekeepers," whose primary role is to assist corporations in complying with burgeoning AI regulations. These gatekeepers are becoming crucial as various jurisdictions, including US states and the European Union, begin to legislate specific AI use-cases. However, the article from Oxford Law Blogs points out several fundamental distinctions that make the regulation of AI uniquely challenging compared to established fields like financial markets. A primary hurdle lies in the inherent ambiguity surrounding core AI concepts. Terms such as 'fairness' and 'explainability,' which are critical for assessing AI systems, lack universally agreed-upon definitions even among leading AI researchers. This conceptual fluidity stands in stark contrast to the well-defined principles found in areas like accounting, making it difficult to establish clear, consistent regulatory standards. The dynamic and often vague nature of nascent AI laws further exacerbates this issue, frequently delegating authority to standard-setting bodies that themselves grapple with these definitional challenges. Moreover, the regulatory landscape for AI currently lacks an institutional analogue to the independent auditors prevalent in financial markets. These traditional gatekeepers provide a crucial layer of oversight and assurance. The absence of such established, independent bodies in the AI sector means that new models for accreditation and oversight are needed. The article identifies three types of emerging AI gatekeepers: those leveraging existing reputational capital from traditional financial services, specialist new entrants focused solely on AI compliance, and institutions granted specific roles by AI legislation, such as notified bodies under the EU AI Act. The authors argue that simply transferring strict gatekeeper liability models from financial markets to AI regulation is not a suitable approach at present. The unique technical and ethical complexities of AI, combined with the immaturity of its regulatory framework, necessitate a more nuanced strategy. They suggest that future governance requirements should focus on transparency regarding the "rules of the game" to allow producers and users to anticipate and adapt to compliance expectations early in the product design phase. Additionally, they propose that accreditation and independence requirements, potentially modeled on the EU's notified-body regime, could ensure competence and mitigate conflicts of interest among AI gatekeepers. This approach aims to foster a more reliable and ethically sound AI ecosystem as the technology continues to evolve.