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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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11129015/ |
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| _version_ | 1849223085986676736 |
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| author | Tiago Brasileiro Araujo Vasilis Efthymiou Kostas Stefanidis |
| author_facet | Tiago Brasileiro Araujo Vasilis Efthymiou Kostas Stefanidis |
| author_sort | Tiago Brasileiro Araujo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-18b6ca95c2dc445bad9e4235c54d2bae |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-18b6ca95c2dc445bad9e4235c54d2bae2025-08-25T23:12:33ZengIEEEIEEE Access2169-35362025-01-011314512714514310.1109/ACCESS.2025.359999011129015Fairness and Explanations in Entity Resolution: An OverviewTiago Brasileiro Araujo0https://orcid.org/0000-0001-6339-9117Vasilis Efthymiou1https://orcid.org/0000-0002-0683-030XKostas Stefanidis2https://orcid.org/0000-0003-1317-8062Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, FinlandDepartment of Informatics and Telematics, Harokopio University of Athens, 176 76, Athens, GreeceFaculty of Information Technology and Communication Sciences, Tampere University, Tampere, FinlandEntity 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.https://ieeexplore.ieee.org/document/11129015/Artificial intelligenceentity resolutionexplainable artificial intelligencefairnessstreaming data |
| spellingShingle | Tiago Brasileiro Araujo Vasilis Efthymiou Kostas Stefanidis Fairness and Explanations in Entity Resolution: An Overview IEEE Access Artificial intelligence entity resolution explainable artificial intelligence fairness streaming data |
| title | Fairness and Explanations in Entity Resolution: An Overview |
| title_full | Fairness and Explanations in Entity Resolution: An Overview |
| title_fullStr | Fairness and Explanations in Entity Resolution: An Overview |
| title_full_unstemmed | Fairness and Explanations in Entity Resolution: An Overview |
| title_short | Fairness and Explanations in Entity Resolution: An Overview |
| title_sort | fairness and explanations in entity resolution an overview |
| topic | Artificial intelligence entity resolution explainable artificial intelligence fairness streaming data |
| url | https://ieeexplore.ieee.org/document/11129015/ |
| work_keys_str_mv | AT tiagobrasileiroaraujo fairnessandexplanationsinentityresolutionanoverview AT vasilisefthymiou fairnessandexplanationsinentityresolutionanoverview AT kostasstefanidis fairnessandexplanationsinentityresolutionanoverview |