Un-observable AI is Un-trustworthy AI

Unobservable AI poses significant trust issues as its unpredictable execution paths and decision-driven resource usage make it difficult to trace and understand its actions, similar to how complex systems like airplanes rely on surrounding systems for trust. The technology's inherent properties mean that trust must be built through observability, which is achieved by instrumenting AI models to produce evidence of their behavior. The OpenTelemetry framework provides the foundation for this, allowing different stakeholders, including users, leadership, and developers, to obtain the necessary evidence to trust AI applications. This includes capturing metrics related to development, operational performance, decision paths, and quality, with tools like Coralogix offering end-to-end support. By implementing these observability layers, organizations can prepare for future scrutiny, ensuring they have concrete data to address trust concerns, defend AI investments, and facilitate debugging processes.

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