🤖 AI Summary
Composite indicators in multi-criteria evaluation often suffer from a “black-box” nature, undermining transparency, interpretability, and comprehensibility.
Method: This paper proposes a dominance-based rough set approach (DRSA)-driven methodology for constructing interpretable composite indicators. It systematically integrates DRSA with four key interpretability requirements—score explanation, quantile-based classification, preference modeling, and result traceability—via ordinal qualitative coding and threshold-based decision rule induction, yielding human-readable “if–then” rules that explicitly link indicator thresholds to categories or scores.
Contribution/Results: Unlike conventional statistical aggregation paradigms, the proposed method enables a paradigm shift toward logic-driven, rule-based evaluation. It significantly enhances transparency, auditability, and the capacity for automatic classification and attribution of new evaluation units, thereby supporting accountable and explainable decision-making in complex multi-criteria assessment contexts.
📝 Abstract
Composite indicators are widely used to score or classify units evaluated on multiple criteria. Their construction involves aggregating criteria evaluations, a common practice in Multiple Criteria Decision Aiding (MCDA). In MCDA, various methods have been proposed to address key aspects of multiple criteria evaluations, such as the measurement scales of the criteria, the degree of acceptable compensation between them, and their potential interactions. However, beyond producing a final score or classification, it is essential to ensure the explainability and interpretability of results as well as the procedure's transparency. This paper proposes a method for constructing explainable and interpretable composite indicators using"if..., then..."decision rules. We consider the explainability and interpretability of composite indicators in four scenarios: (i) decision rules explain numerical scores obtained from an aggregation of numerical codes corresponding to ordinal qualifiers; (ii) an obscure numerical composite indicator classifies units into quantiles; (iii) given preference information provided by a Decision Maker in the form of classifications of some reference units, a composite indicator is constructed using decision rules; (iv) the classification of a set of units results from the application of an MCDA method and is explained by decision rules. To induce the rules from scored or classified units, we apply the Dominance-based Rough Set Approach. The resulting decision rules relate the class assignment or unit's score to threshold conditions on values of selected indicators in an intelligible way, clarifying the underlying rationale. Moreover, they serve to recommend composite indicator assessment for new units of interest.