We believe that useful AI must be explainable. Our system gives exact explanations for its decisions. An explanation is based on the previously verified cases that were used to generate the new decision. The explanation is detailed, easily understood by non-technical users, and can be used to document the decision.
Knowledge as a tangible asset
The system learns as it processes new cases, building a knowledge base. This knowledge base is human-readable and can be used to analyse the knowledge assets of an organisation.
Unlike machine learning, our systems gives users control over the decision process. Users can decide exactly how the knowledge base is built and what knowledge is included in the decision making. Therefore, biased decision can be easily detected and avoided.
“True ignorance is not the absence of knowledge, but the refusal to acquire it.”
— Karl Popper