usulise

Usulise is a theoretical framework for designing and evaluating rule-based reasoning systems that explicitly integrate human-authored rules with data-driven inference. In its ideal form, the framework treats rules as primary sources of decision logic, while empirical data serves to test, tune, and validate rule performance without allowing data alone to determine outcomes.

Etymology and concept: The term is a coinage, drawing on usul, a word used in several Islamic and philosophical traditions to mean "principles" or "foundations," combined with the English suffix -ise to indicate process or method. In practice, usulise emphasizes principled rule construction, traceability, and repeatable evaluation.

Components: A typical usulise system comprises a rule base, a reasoning engine, a provenance ledger to document rule origins and changes, and an evaluation module that compares rule-driven results to observed outcomes. The process supports iterative refinement, auditing, and transparency.

Development and use: Usulise emerged in academic discussions of explainable artificial intelligence and governance frameworks in the mid-2010s and has since been explored in both software engineering and policy design contexts. Proponents argue that the approach improves accountability and regulatory compliance by making decision logic explicit.

Applications: Usulise has been proposed for risk assessment, compliance auditing, content moderation, and automated decision systems where stakeholders require understandable rationale and reproducibility.

Strengths and challenges: Advantages include interpretability, modularity, and safer integration of human judgment. Challenges include scalability for large rule bases, maintaining consistency between rules and evolving data, and the potential for rule conflicts or rigidity in fast-changing domains.

See also: Rule-based systems, explainable AI, governance, provenance, logic and computation.