Why Enterprise AI Needs Ontology Before It Needs More Models
This article discusses how traditional testing and validation methods are insufficient for ensuring the correctness of enterprise AI systems, and how an ontology-driven governance approach can help address the problem of
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Why it matters
This article highlights a critical challenge in building robust and reliable enterprise AI systems, and proposes a novel approach to governance and verification that can help address this problem.
Key Points
- 1A real-world case study of a seemingly robust AI system with 22 hidden failures that went undetected by 610 unit tests, 98/100 security score, and 4 validation layers
- 2The pattern of
- 3 where a system's declared state (documentation, config, registry) differs from its actual runtime behavior
- 4Traditional testing methods like unit tests and integration tests are insufficient for catching these types of issues
- 5Ontology-driven governance - a formal declaration of system invariants and executable checks to verify them - as a solution to this problem
Details
The article presents a real-world case study of an AI system that had seemingly robust testing and validation in place, yet still had 22 hidden failures that went undetected. The core issue was a pattern of
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