# Threshold-Independent Fair Matching through Score Calibration

arXiv: https://arxiv.org/abs/2405.20051 · HTML: https://mlsanity.com/paper/2405.20051/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 30 May 2024.

## Builds on (most cited first, 9 of 9)

- [Wasserstein Fair Classification](https://mlsanity.com/paper/1907.12059/) (2019)
- [Fair Regression with Wasserstein Barycenters](https://mlsanity.com/paper/2006.07286/) (2020)
- [The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metric](https://mlsanity.com/paper/1902.05826/) (2019)
- [Modeling Voting for System Combination in Machine Translation](https://mlsanity.com/paper/2007.06943/) (2020)
- [Minimax AUC Fairness: Efficient Algorithm with Provable Convergence](https://mlsanity.com/paper/2208.10451/) (2022)
- [The Misuse of AUC: What High Impact Risk Assessment Gets Wrong](https://mlsanity.com/paper/2305.18159/) (2023)
- [Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching](https://mlsanity.com/paper/2307.02726/) (2023)
- [Repairing Regressors for Fair Binary Classification at Any Decision Threshold](https://mlsanity.com/paper/2203.07490/) (2022)
- [Online Algorithms for Matchings with Proportional Fairness Constraints and Diversity Constraints](https://mlsanity.com/paper/2208.11378/) (2022)

## Built on by (most cited first, 4 of 4)

- [Evaluating Blocking Biases in Entity Matching](https://mlsanity.com/paper/2409.16410/) (2024)
- [Beyond Accuracy: An Empirical Study of Uncertainty Estimation in Imputation](https://mlsanity.com/paper/2511.21607/) (2025)
- [Unbiased Binning for Fairness-aware Attribute Representation](https://mlsanity.com/paper/2509.21785/) (2025)
- [Reducing Biases in Record Matching Through Scores Calibration](https://mlsanity.com/paper/2411.01685/) (2024)

## Abstract (arXiv)

Entity Matching (EM) is a critical task in numerous fields, such as healthcare, finance, and public administration, as it identifies records that refer to the same entity within or across different databases. EM faces considerable challenges, particularly with false positives and negatives. These are typically addressed by generating matching scores and apply thresholds to balance false positives and negatives in various contexts. However, adjusting these thresholds can affect the fairness of the outcomes, a critical factor that remains largely overlooked in current fair EM research. The existing body of research on fair EM tends to concentrate on static thresholds, neglecting their critical impact on fairness. To address this, we introduce a new approach in EM using recent metrics for evaluating biases in score based binary classification, particularly through the lens of distributional parity. This approach enables the application of various bias metrics like equalized odds, equal opportunity, and demographic parity without depending on threshold settings. Our experiments with leading matching methods reveal potential biases, and by applying a calibration technique for EM scores using Wasserstein barycenters, we not only mitigate these biases but also preserve accuracy across real world datasets. This paper contributes to the field of fairness in data cleaning, especially within EM, which is a central task in data cleaning, by promoting a method for generating matching scores that reduce biases across different thresholds.