# Evaluating Blocking Biases in Entity Matching

arXiv: https://arxiv.org/abs/2409.16410 · HTML: https://mlsanity.com/paper/2409.16410/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 44 references that are in the map.
- First posted on arXiv 24 Sep 2024.

## Builds on (most cited first, 11 of 11)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment](https://mlsanity.com/paper/1610.08452/) (2016)
- [Deep Entity Matching with Pre-Trained Language Models](https://mlsanity.com/paper/2004.00584/) (2020)
- [DeepER -- Deep Entity Resolution](https://mlsanity.com/paper/1710.00597/) (2017)
- [Modeling Voting for System Combination in Machine Translation](https://mlsanity.com/paper/2007.06943/) (2020)
- [Pre-trained Embeddings for Entity Resolution: An Experimental Analysis [Experiment, Analysis & Benchmark]](https://mlsanity.com/paper/2304.12329/) (2023)
- [AutoBlock: A Hands-off Blocking Framework for Entity Matching](https://mlsanity.com/paper/1912.03417/) (2019)
- [Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching](https://mlsanity.com/paper/2307.02726/) (2023)
- [A Comparison of Blocking Methods for Record Linkage](https://mlsanity.com/paper/1407.3191/) (2014)
- [Threshold-Independent Fair Matching through Score Calibration](https://mlsanity.com/paper/2405.20051/) (2024)
- [Efficient and Effective ER with Progressive Blocking](https://mlsanity.com/paper/2005.14326/) (2020)

## Built on by (most cited first, 3 of 3)

- [Cost-Efficient RAG for Entity Matching with LLMs: A Blocking-based Exploration](https://mlsanity.com/paper/2602.05708/) (2026)
- [SeDa: A Unified System for Dataset Discovery and Multi-Entity Augmented Semantic Exploration](https://mlsanity.com/paper/2603.07502/) (2026)
- [Corporate-Family Resolution Is Not a String-Matching Problem: A Public Benchmark Stratified by Name Visibility](https://mlsanity.com/paper/2609.04269/) (2026)

## Abstract (arXiv)

Entity Matching (EM) is crucial for identifying equivalent data entities across different sources, a task that becomes increasingly challenging with the growth and heterogeneity of data. Blocking techniques, which reduce the computational complexity of EM, play a vital role in making this process scalable. Despite advancements in blocking methods, the issue of fairness; where blocking may inadvertently favor certain demographic groups; has been largely overlooked. This study extends traditional blocking metrics to incorporate fairness, providing a framework for assessing bias in blocking techniques. Through experimental analysis, we evaluate the effectiveness and fairness of various blocking methods, offering insights into their potential biases. Our findings highlight the importance of considering fairness in EM, particularly in the blocking phase, to ensure equitable outcomes in data integration tasks.