Recall

Recall is a model performance metric measuring the proportion of actual positive cases that the model correctly identifies -- in other words, of all the real positives, how many did the model catch? High recall means few false negatives (missed cases). Recall is critical in applications where...

Detailed Explanation

Recall is a model performance metric measuring the proportion of actual positive cases that the model correctly identifies -- in other words, of all the real positives, how many did the model catch? High recall means few false negatives (missed cases). Recall is critical in applications where missing a positive case is dangerous or costly: a disease screening tool with low recall misses patients who need treatment, a fraud detection system with low recall allows fraudulent transactions through, or a quality inspection system with low recall passes defective products. Recall and precision often have an inverse relationship, and the appropriate balance depends on the business context. In high-stakes applications like healthcare or safety, organizations typically prioritize recall even at the cost of more false positives.

Why It Matters

Understanding Recall is essential for organizations pursuing responsible AI transformation. In the context of enterprise AI governance, this concept directly impacts how organizations design, deploy, and oversee AI systems particularly within the Technology pillar. Without a clear grasp of Recall, organizations risk creating governance gaps that undermine trust, compliance, and long-term value realization. For AI leaders and practitioners, Recall provides the conceptual foundation needed to make informed decisions about AI strategy, risk management, and stakeholder engagement. As regulatory frameworks such as the EU AI Act and standards like ISO 42001 mature, proficiency in concepts like Recall becomes not merely advantageous but operationally necessary for any organization deploying AI at scale.

COMPEL-Specific Usage

Technical concepts map to the Technology pillar of the COMPEL framework. They are most relevant during the Model stage (designing AI system architecture and governance controls) and the Produce stage (building, testing, and deploying AI solutions). COMPEL ensures that technical decisions are never made in isolation but are governed by the broader organizational context of People, Process, and Governance pillars. The concept of Recall is most directly applied during the Model and Produce stages of the COMPEL operating cycle. Practitioners preparing for COMPEL certification will encounter Recall in coursework aligned with the Technology pillar, and should be prepared to demonstrate applied understanding during assessment activities.

Related Standards & Frameworks

• ISO/IEC 42001:2023 Annex A.5 (AI System Inventory)
• NIST AI RMF MAP and MEASURE functions
• IEEE 7000-2021