X-TREATS: Integrating explainability and fairness into streaming entity resolution

Entity Resolution (ER) is a fundamental task in data integration, particularly in streaming environments where entities arrive continuously and decisions must be made under strict time constraints. Existing approaches primarily optimize efficiency and accuracy, but often overlook the interpretability of matching decisions and may propagate group-level disparities. To address these limitations, this paper introduces X-TREATS, a streaming-oriented ER workflow that integrates pair-level explanations directly into the resolution pipeline, jointly combining similarity, fairness, and explanation signals during ranking step. The proposed approach is evaluated on real-world datasets under incremental processing, assessing effectiveness, fairness, and explainability. Experimental results show that X-TREATS substantially improves interpretability, increasing the Explanation Score by up to 20%, while reducing group-level disparities by 40–70% and preserving high matching precision across all datasets. These findings demonstrate the practical benefits of integrating explanation-aware mechanisms into real-time ER pipelines.