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The paper describes a Multisource AI Scorecard Table (MAST) that provides thedeveloper and user of an artificial intelligence (AI)/machine learning (ML)system with a standard checklist focused on the principles of good analysisadopted by the intelligence community (IC) to help promote the development ofmore understandable systems and engender trust in AI outputs. Such a scorecardenables a transparent, consistent, and meaningful understanding of AI toolsapplied for commercial and government use. A standard is built on complianceand agreement through policy, which requires buy-in from the stakeholders.While consistency for testing might only exist across a standard data set, thecommunity requires discussion on verification and validation approaches whichcan lead to interpretability, explainability, and proper use. The paperexplores how the analytic tradecraft standards outlined in IntelligenceCommunity Directive (ICD) 203 can provide a framework for assessing theperformance of an AI system supporting various operational needs. These includesourcing, uncertainty, consistency, accuracy, and visualization. Three usecases are presented as notional examples that support security for comparativeanalysis.

Multisource AI Scorecard Table for System Evaluation