Measurement Before Control: A Broadly Applicable Principle for Better AI Policy (medium.com)

In a groundbreaking approach to AI policy, researchers have proposed a principle dubbed "Measurement Before Control," advocating for the establishment of robust measurement systems before implementing regulatory controls. This recommendation emerges from the understanding that comprehensive data is essential for informed decision-making in AI governance. By prioritizing measurement, policymakers can ensure they first grasp the intricacies and impacts of AI technologies, leading to more effective and targeted regulations. The significance of this principle lies in its potential to revolutionize how we approach AI governance, addressing the complexities and rapid advancements in machine learning systems. In practice, this means creating standards for data collection and analysis, which could provide insights into AI’s societal implications, biases, and performance. By focusing on quantifying outcomes and effects before introducing controls, policymakers can create a responsive regulatory landscape that adjusts to the evolving nature of technology, ultimately fostering innovation while safeguarding public interests.