FairEM360: A Suite for Responsible Entity Matching
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910534712950784 |
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| author | Shahbazi, Nima Erfanian, Mahdi Asudeh, Abolfazl Nargesian, Fatemeh Srivastava, Divesh |
| author_facet | Shahbazi, Nima Erfanian, Mahdi Asudeh, Abolfazl Nargesian, Fatemeh Srivastava, Divesh |
| contents | Entity matching is one the earliest tasks that occur in the big data pipeline and is alarmingly exposed to unintentional biases that affect the quality of data. Identifying and mitigating the biases that exist in the data or are introduced by the matcher at this stage can contribute to promoting fairness in downstream tasks. This demonstration showcases FairEM360, a framework for 1) auditing the output of entity matchers across a wide range of fairness measures and paradigms, 2) providing potential explanations for the underlying reasons for unfairness, and 3) providing resolutions for the unfairness issues through an exploratory process with human-in-the-loop feedback, utilizing an ensemble of matchers. We aspire for FairEM360 to contribute to the prioritization of fairness as a key consideration in the evaluation of EM pipelines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_07354 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FairEM360: A Suite for Responsible Entity Matching Shahbazi, Nima Erfanian, Mahdi Asudeh, Abolfazl Nargesian, Fatemeh Srivastava, Divesh Databases Computers and Society Machine Learning Entity matching is one the earliest tasks that occur in the big data pipeline and is alarmingly exposed to unintentional biases that affect the quality of data. Identifying and mitigating the biases that exist in the data or are introduced by the matcher at this stage can contribute to promoting fairness in downstream tasks. This demonstration showcases FairEM360, a framework for 1) auditing the output of entity matchers across a wide range of fairness measures and paradigms, 2) providing potential explanations for the underlying reasons for unfairness, and 3) providing resolutions for the unfairness issues through an exploratory process with human-in-the-loop feedback, utilizing an ensemble of matchers. We aspire for FairEM360 to contribute to the prioritization of fairness as a key consideration in the evaluation of EM pipelines. |
| title | FairEM360: A Suite for Responsible Entity Matching |
| topic | Databases Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2404.07354 |