AI Contextual Evaluations is Humane Intelligence’s term for highly customized, bespoke, and comprehensive analysis of an AI model or system’s performance in a specified problem space. Our AI contextual evaluations use mixed-methods, and can contain any number of AI red teaming workshops, bias bounties, knowledge graphs / ontologies, benchmarks or other evaluation types.
One of the most common questions Humane Intelligence clients ask is, “How do I know if an AI model or system performs well in a particular use case?” When we unpack this statement, we see that accounting for AI contextual evaluation complexities can be difficult. Guessing which scenarios to test in an AI red teaming workshop or examining underlying data without a clear direction is insufficient. Similarly, a static taxonomy of harms may not always be adequate to capture the breadth of possible failure points. That’s why in 2026, Humane Intelligence is rapidly developing a knowledge graph / ontology based approach for AI contextual evaluations.
With our new knowledge graph / ontological based methodology, Humane Intelligence is helping clients better understand their problem space coverage and gaps, and how to use compute and resources more effectively. This approach creates more mathematical and statistical rigor and clarity around AI model and systems performance, evaluation replicability and limitations, and ultimately, better product go/no-go decisions.
At the link below, we discuss ontologies as it pertains to the Sustainable Development Goals (SDGs). Our knowledge graph / ontology methodology blog post is forthcoming.
Every AI contextual evaluation is different! While there’s no single way to determine which is better, here’s a quick guide to decide whether a taxonomical or ontological based methodology is more appropriate:
Our AI contextual evaluations have helped our past clients answer:
SINGAPORE IMDA
Humane Intelligence worked with the Singaporean Infocomm Media Development Authority (IMDA) on a red teaming event and contextual evaluation, covering nine languages and drawing participants from across ASEAN.