Today, AI relies on data, and many organizations are treating AI systems like traditional applications. From my experience leading large AI and data modernization projects in regu ...

In traditional enterprise architecture, no application is allowed to execute privileged actions without passing through layers of policy enforcement, access validation, logging, and governance.

Explainability tools are commonly used in AI development to provide visibility into how models interpret data. In healthcare machine learning systems, explainability techniques may highlight factors ...

Health system initiatives fail due to weak governance and lack of readiness, risking patient safety and organizational integrity in AI adoption.

AI ethics researcher and governance expert Fabrizio Degni has introduced the PALO (Principled AI Lifecycle Orchestration) framework, a governance model designed to help organizations operationalize ...

As health systems expand their use of AI, many are establishing governance structures to ensure the technology is safe, effective and ethically sound. Here’s a look at how two health systems — Mercy ...

In the evolving landscape of higher education, institutions are grappling with how to adapt their governance models to address new trends and technologies. The traditional balance of power and ...

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Agencies across the government, including in the White House, are planning for, procuring, building, or attempting to scale impact and improve performance through integrating data and using data ...