Actionable Principles for Artificial Intelligence Policy: Three Pathways

Ethics principles alone aren't enough: The paper identifies a critical gap where AI Ethics Principles—while well-intentioned—often fail to translate into actual governmental policy. Simply stating values like "fairness" or "transparency" doesn't automatically lead to real-world implementation.

Three practical pathways can bridge the gap: The authors propose a framework with three concrete steps: (1) conducting preliminary landscape assessments to understand the current state of AI regulation, (2) involving multiple stakeholders across different sectors in principle development, and (3) building in specific mechanisms to ensure principles can actually be operationalized and monitored.

Multi-stakeholder input is essential: Drawing from the European Commission's Ethics Guidelines development process, the paper emphasizes that policymakers should actively seek feedback from diverse groups—not just AI experts—to ensure principles address real-world concerns and are practically feasible across different industries and contexts.

Implementation must be designed from the start: Rather than developing principles first and figuring out how to enforce them later, the framework suggests that policymakers should consider how principles will actually be measured, monitored, and enforced while they're still being developed.