Knowledge-augmented Risk Assessment (KaRA): a hybrid-intelligence framework for supporting knowledge-intensive risk assessment of prospect candidates
This addresses risk assessment for decision-makers in industries like oil and materials, but it appears incremental as it integrates existing AI methods with expert knowledge.
The paper tackles the problem of biased and inconsistent Probability of Success assessments in prospect evaluation by developing the KaRA framework, which combines AI techniques with expert feedback and a structured knowledge-base to support risk assessment.
Evaluating the potential of a prospective candidate is a common task in multiple decision-making processes in different industries. We refer to a prospect as something or someone that could potentially produce positive results in a given context, e.g., an area where an oil company could find oil, a compound that, when synthesized, results in a material with required properties, and so on. In many contexts, assessing the Probability of Success (PoS) of prospects heavily depends on experts' knowledge, often leading to biased and inconsistent assessments. We have developed the framework named KARA (Knowledge-augmented Risk Assessment) to address these issues. It combines multiple AI techniques that consider SMEs (Subject Matter Experts) feedback on top of a structured domain knowledge-base to support risk assessment processes of prospect candidates in knowledge-intensive contexts.