explnOR

ExplnOR is a framework used in explainable artificial intelligence to construct and present explanations for automated decisions by combining multiple explanation sources with a disjunctive operator. The central idea is to generate a set of plausible reasons from diverse explainers and to present them as alternative explanations rather than forcing a single narrative. This enables users to inspect different rationales that could justify a decision and supports accountability when multiple stakeholders require different justification styles.

Origin and scope: The term emerged in discussions of explainability architectures in the 2020s. While not tied to a single implementation, explnOR has been described as an approach for modular, plug-in explainers that feed a common OR-combiner.

Architecture and workflow: The typical pipeline includes (1) data and model outputs, (2) independently generated explanations from one or more explainers (rule-based, feature-importance, example-based, counterfactual), (3) an OR-based combiner that assembles a disjunction of the explanations into a cohesive set, (4) a natural language generator to translate the explanations for end users, and (5) an audit log to record sources and rationale.

Benefits and limitations: Benefits include improved transparency through multiple perspectives and easier auditing. Limitations include potential explanation overload, possible conflicts between alternatives, and the need for careful user interface design to avoid misinterpretation.

Applications: ExplnOR is discussed for risk assessment, regulated industries, and scenarios where diverse stakeholder explanations are valued, such as lending decisions, medical triage, and compliance reporting.

See also: Explainable AI, interpretable machine learning, model auditing, counterfactual explanations.