AI Compliance: Overcoming the Challenges of Scoping Gap Analysis

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AI Regulation Scope

 

A significant pitfall that exacerbates the difficulty of conducting effective AI regulation gap analyses is the challenge in determining the appropriate scope of analysis amidst the variety and superposition of AI regulations.

The Pitfall:

This complexity arises from the fact that AI applications often span multiple jurisdictions and regulatory domains, each with its own set of rules and guidelines.

For instance, an AI solution deployed in a global wealth management company will have to comply not only with the European Union's regulation but also with varying laws and framework in other regions, such as the pro- innovative approach in the UK, the Executive Order in the US or the MAS Framework  in Singapore.

Moreover, the superposition of AI regulations refers to the overlapping and sometimes conflicting requirements from different regulatory bodies or within different legal frameworks. 

For example, the ethical use of AI in healthcare involves navigating MDR (Medical Device Regulation) and the EU AI Act. Both regulations classify medical devices into risk categories, with higher-risk categories subject to more stringent regulatory requirements. AI systems utilized within medical devices, particularly those classified as high-risk, would need to adhere to both sets of requirements.

This variety and superposition create a complex puzzle for organizations trying to determine the full scope of their regulatory obligations. It's not just a matter of complying with one set of laws but understanding how multiple sets intersect, overlap, and sometimes contradict each other. 

 

Integrating the Lessons

 

To navigate this pitfall, organizations need to take several steps:

  • Conduct a Jurisdictional Analysis: Understand which laws and regulations apply based on the geographic locations where the AI technology is developed, deployed, or used. This includes both the locations of the data subjects and the physical locations of data processing and storage.
  • Map the Regulatory Landscape: Create a comprehensive map of all relevant AI regulations, guidelines, and ethical standards, identifying areas of overlap and potential conflict. This mapping should be revisited regularly as laws and regulations evolve.
  • Develop a strategic approach to compliance that seeks to satisfy the broadest set of requirements efficiently.
  • Include diverse perspectives: AI regulations are not just legal issues but are deeply intertwined with technical, data, societal and ethical considerations. Without the inclusion of diverse perspectives—from legal experts to AI data scientists, CDOs, IT security experts and ethicists—organizations miss out on a holistic understanding of the implications of AI use within their operations.

 

Addressing the pitfall of determining the scope of gap analysis due to the variety and superposition of AI regulations requires a proactive, informed, multidisciplinary and strategic approach. 

By recognizing and addressing this complexity, organizations can better navigate the complex network of AI regulations, ensuring compliance and fostering innovation in AI use.

 

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