Fairness and Explanations in Entity Resolution: An Overview
Entity Resolution (ER) is a fundamental task in data integration, enabling the identification of records that refer to the same real-world entity across diverse and often heterogeneous data sources. Recent advances in Artificial Intelligence (AI) have significantly improved ER performance, particula...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11129015/ |
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| Summary: | Entity Resolution (ER) is a fundamental task in data integration, enabling the identification of records that refer to the same real-world entity across diverse and often heterogeneous data sources. Recent advances in Artificial Intelligence (AI) have significantly improved ER performance, particularly with deep learning and pre-trained embeddings. However, these AI-driven solutions introduce new challenges related to fairness and explainability. Fairness-aware ER seeks to mitigate bias that may arise from algorithmic decision-making or imbalanced training data, while eXplainable Entity Resolution (XER) aims to enhance transparency and trust in ER. In this work, we provide a comprehensive overview of fairness and explainability in ER, systematically analyzing existing techniques across the ER pipeline. We discuss challenges in ensuring unbiased and interpretable ER outcomes, with a special focus on streaming environments, where real-time decision-making intensifies the complexity of these concerns. Furthermore, we outline research opportunities and examine the trade-offs between schema-aware and schema-agnostic methods, as well as rule-based and machine learning-based comparison techniques, in ensuring fair and transparent ER. Our study highlights open research challenges and potential future directions, encouraging novel explainable AI methodologies and fairness-aware ER solutions that enhance the reliability, accountability, and societal impact of AI-driven ER systems. |
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| ISSN: | 2169-3536 |