# Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching

arXiv: https://arxiv.org/abs/2307.02726 · HTML: https://mlsanity.com/paper/2307.02726/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 74 references that are in the map.
- First posted on arXiv 6 Jul 2023.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018)
- [Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees](https://mlsanity.com/paper/1806.06055/) (2018)
- [Deep Entity Matching with Pre-Trained Language Models](https://mlsanity.com/paper/2004.00584/) (2020)
- [Designing Fair Ranking Schemes](https://mlsanity.com/paper/1712.09752/) (2017)
- [Representation Bias in Data: A Survey on Identification and Resolution Techniques](https://mlsanity.com/paper/2203.11852/) (2022)
- [Neural Networks for Entity Matching: A Survey](https://mlsanity.com/paper/2010.11075/) (2020)
- [Assessing and Remedying Coverage for a Given Dataset](https://mlsanity.com/paper/1810.06742/) (2018)
- [Modeling Voting for System Combination in Machine Translation](https://mlsanity.com/paper/2007.06943/) (2020)
- [Machamp: A Generalized Entity Matching Benchmark](https://mlsanity.com/paper/2106.08455/) (2021)
- [A Survey of Blocking and Filtering Techniques for Entity Resolution](https://mlsanity.com/paper/1905.06167/) (2019)
- [Maximizing Fair Content Spread via Edge Suggestion in Social Networks](https://mlsanity.com/paper/2207.07704/) (2022)
- [LEMON: Explainable Entity Matching](https://mlsanity.com/paper/2110.00516/) (2021)
- [Battling the Digital Forensic Backlog through Data Deduplication](https://mlsanity.com/paper/1610.00248/) (2016)

## Built on by (most cited first, 9 of 9)

- [Chameleon: Foundation Models for Fairness-aware Multi-modal Data Augmentation to Enhance Coverage of Minorities](https://mlsanity.com/paper/2402.01071/) (2024)
- [Threshold-Independent Fair Matching through Score Calibration](https://mlsanity.com/paper/2405.20051/) (2024)
- [Evaluating Blocking Biases in Entity Matching](https://mlsanity.com/paper/2409.16410/) (2024)
- [FairEM360: A Suite for Responsible Entity Matching](https://mlsanity.com/paper/2404.07354/) (2024)
- [Unbiased Binning for Fairness-aware Attribute Representation](https://mlsanity.com/paper/2509.21785/) (2025)
- [Be Fair! Can Machine Learning Engineering Agents Adhere to Fairness Constraints?](https://mlsanity.com/paper/2606.04971/) (2026)
- [CaRL-EM: Cost-Aware Reinforcement Learning for Entity Matching with LLMs](https://mlsanity.com/paper/2609.01195/) (2026)
- [Reducing Biases in Record Matching Through Scores Calibration](https://mlsanity.com/paper/2411.01685/) (2024)
- [Beyond Scale and Generation: Understanding Language Model-based Entity Matching](https://mlsanity.com/paper/2607.24688/) (2026)

## Abstract (arXiv)

Entity matching (EM) is a challenging problem studied by different communities for over half a century. Algorithmic fairness has also become a timely topic to address machine bias and its societal impacts. Despite extensive research on these two topics, little attention has been paid to the fairness of entity matching. Towards addressing this gap, we perform an extensive experimental evaluation of a variety of EM techniques in this paper. We generated two social datasets from publicly available datasets for the purpose of auditing EM through the lens of fairness. Our findings underscore potential unfairness under two common conditions in real-world societies: (i) when some demographic groups are overrepresented, and (ii) when names are more similar in some groups compared to others. Among our many findings, it is noteworthy to mention that while various fairness definitions are valuable for different settings, due to EM's class imbalance nature, measures such as positive predictive value parity and true positive rate parity are, in general, more capable of revealing EM unfairness.