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Demetri Giannikopoulos

AI Governance: Regulation and Governance Hand-Shake

Data Management, Outcomes
Table of Contents

Peanut butter and jelly. Batman and Robin. Keyboard and mouse. 

The world has plenty of complementary pairs, and clinical AI is no exception. While healthcare and artificial intelligence may offer an obvious choice for a dynamic duo, another collaboration is just as important for AI’s long-term success: AI governance and AI regulation.

The proverbial hand-shake between governance and regulation emphasizes the interdependent relationship between internal organizational controls and external frameworks to ensure the ethical and responsible use of AI. Let’s explore why you can’t have one without the other.

Regulation vs. Governance: Understanding the Difference

While there is some overlap in intent, regulation and governance serve distinct purposes and should be understood as separate but interconnected guiding structures.

  • Regulation refers to external rules and standards imposed by government or industry bodies to govern AI development and deployment. These laws or standards aim to protect patients, ensure fairness and prevent misuse. Examples include data privacy regulations, like GDPR and HIPAA, as well as AI-specific directives, like FDA, EO 14110 and HTI-1.
  • Governance is an organization’s internal framework for managing AI risks and ensuring ethical practices. It involves creating policies, procedures and committees to oversee AI development, deployment and use. The scope includes risk assessments, data and decision transparency and reporting as well as determining future needs.

Why Regulation and Governance Are a Clinical AI Dynamic Duo

While regulation provides a baseline for responsible AI, it’s often all-encompassing. Internal governance complements regulation by tailoring it to an organization’s specific use cases, culture and risk comfort. Together, they form a consistent framework that emphasizes patient safety and quality care:

  • Guardrails: Regulations set the boundaries, while governance ensures uniform compliance.
  • Risk Mitigation: A strong governance framework proactively identifies and manages risks before they escalate into a regulatory issue.
  • Trust: Aligning governance with regulations demonstrates a commitment to responsible AI, fostering trust among stakeholders.
  • Innovation: Staying current on regulatory changes is crucial for a governance structure that fosters ongoing innovation.

The Key to a Good Hand-Shake

A successful partnership between AI governance and AI regulation requires open communication and collaboration. 

Regulatory entities should seek input from people who use and benefit from AI (end-users and patients) to understand the real-world impact and challenges. While health systems should regularly assess technology against the latest regulations and guidelines to ensure responsible use. 

Organizations can build a strong hand-shake by aligning internal governance with external regulations. This involves:

  • Building a governance framework that is adaptable 
  • Staying up-to-date on regulatory changes and updating policies and training accordingly
  • Fostering collaboration between internal and external teams
  • Prioritizing transparency and accountability with the technology
  • Protecting data (this will always be an important component of regulations)
  • Implementing a robust change management plan 

Even though this relationship is largely theoretical, creating a synergy between governance and regulation ensures responsible AI development and compliance with legal and ethical standards.

Explore more tips for AI governance with these curated resources. 

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Demetri Giannikopoulos

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