The development and deployment of artificial intelligence (AI) systems require a comprehensive understanding of their capabilities, limitations, and potential impacts. A framework for describing AI work can help researchers and practitioners design evaluations that are technically sound and claim-aware, bridging the gap between measured performance and actual capability.
The framework focuses on validity-centered AI evaluation, considering five forms of validity: content validity, external validity, criterion validity, construct validity, and consequential validity. Content validity ensures that the measurement covers relevant content and generalizes to unmeasured components. External validity examines the generalizability of findings across different populations, environments, or settings.
Criterion validity assesses the relationship between the measurement and a criterion or established standard. Construct validity evaluates the theoretical alignment between the test and its intended construct. Consequential validity considers the potential consequences and impact of AI systems.
To apply this framework, one must establish the maximum residual risk each stakeholder is willing to accept given the system's context and potential impact. This involves defining the scope of the claim and populating the validity framework with markers indicating where evidence is weak, moderate, or strong. The evidence bar should be calibrated to the gravity of potential harm, ensuring that the evaluation is proportionate to the potential risks.
The framework provides a structured approach to assessing the validity of AI assessments, ensuring appropriate use and interpretation. By preventing overgeneralization and supporting more accurate interpretations of results, this framework can help researchers and practitioners design more effective AI systems that are aligned with their intended purposes.
This framework can be applied to various AI domains and tasks, providing a flexible and practical approach to AI evaluation. Its flexibility and adaptability make it a valuable tool for ensuring that AI systems are developed and deployed responsibly, with consideration for their potential impacts on individuals and society.
