expertdefined

Expertdefined is a term used in AI and analytics to describe systems where domain experts actively define the structure, rules, and constraints that govern decisions and outputs. It contrasts with purely data-driven approaches that learn rules implicitly from large datasets.

In an expertdefined workflow, knowledge elicitation gathers domain insights, which are formalized into rule sets, feature definitions, thresholds, or constraint models. These rules are then encoded into a rule engine or integrated with machine learning components to influence prediction, classification, or control tasks. The resulting system combines explicit expert knowledge with data-driven components to improve interpretability and safety.

Applications include healthcare decision support, financial risk scoring, industrial process control, regulatory compliance, and safety-critical monitoring. Examples include prescription safety checks defined by clinicians, hand-tuned thresholds for anomaly detection, or governance rules ensuring outputs align with policy requirements.

Advantages include improved transparency, traceability, and alignment with domain norms; potential to perform well with limited data; and faster initial deployment in regulated settings. Limitations consist of scalability challenges as domain coverage grows; dependence on expert availability and consensus; maintenance burden to keep rules current; and potential conflicts with data-driven learning or brittle behavior when environments shift.

Relation to related concepts includes alignment with rule-based systems and expert systems, often forming hybrids with machine learning models. The approach emphasizes interpretability and controllability and is commonly pursued where regulatory, safety, or policy considerations require explicit, auditable decision logic.