  The paper describes a Multisource AI Scorecard Table (MAST) that provides the
developer and user of an artificial intelligence (AI)/machine learning (ML)
system with a standard checklist focused on the principles of good analysis
adopted by the intelligence community (IC) to help promote the development of
more understandable systems and engender trust in AI outputs. Such a scorecard
enables a transparent, consistent, and meaningful understanding of AI tools
applied for commercial and government use. A standard is built on compliance
and agreement through policy, which requires buy-in from the stakeholders.
While consistency for testing might only exist across a standard data set, the
community requires discussion on verification and validation approaches which
can lead to interpretability, explainability, and proper use. The paper
explores how the analytic tradecraft standards outlined in Intelligence
Community Directive (ICD) 203 can provide a framework for assessing the
performance of an AI system supporting various operational needs. These include
sourcing, uncertainty, consistency, accuracy, and visualization. Three use
cases are presented as notional examples that support security for comparative
analysis.
