Refers to the growing set of organizations, benchmarks, standards, and research efforts used to test and assess AI systems. As AI tools become more widely used in workplaces, education systems, employer hiring processes, and public services, there is increasing interest in ways to evaluate how these technologies perform in real-world settings. AI evaluation efforts examine issues such as model accuracy, reliability, safety, bias, transparency, and how people actually interact with AI tools in practice.

Unlike traditional sectors where “product testing” is centralized or regulated through well-established institutions, the infrastructure for evaluating AI is still emerging. Evaluation activities are currently carried out by a variety of actors, including technology companies that test their own systems, academic researchers studying real-world performance and impacts, independent benchmarking organizations that develop standardized tests for comparing models, and government agencies developing frameworks and guidance for responsible AI deployment.

Several types of organizations participate in this emerging evaluation ecosystem. For example:

• Benchmarking organizations: Develop standardized tests for comparing machine learning models and tracking technological progress.
• Government standards bodies: Publish frameworks to help organizations evaluate and manage risks associated with AI systems.
• Academic research centers: Study how AI performs in real-world environments, including how users interact with these systems and how outcomes vary across contexts.
• Independent research and policy organizations: Examine broader social, economic, and governance implications of AI deployment.

As AI becomes more integrated into education advising systems, hiring platforms, workplace productivity tools, and learning technologies, the development of credible and transparent evaluation systems is increasingly viewed as essential. These efforts help organizations understand how AI functions in practice, identify potential risks, and support more responsible and effective deployment of AI in education, workforce development, and other parts of the learn-and-work ecosystem.

See Topic Brief: AI Evaluation Ecosystem | Learn & Work Ecosystem Library