# Towards Threshold Invariant Fair Classification

arXiv: https://arxiv.org/abs/2006.10667 · HTML: https://mlsanity.com/paper/2006.10667/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2020.

## Built on by (most cited first, 12 of 12)

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- [Directional Bias Amplification](https://mlsanity.com/paper/2102.12594/) (2021)
- [CAT: Controllable Attribute Translation for Fair Facial Attribute Classification](https://mlsanity.com/paper/2209.06850/) (2022)
- [Towards Fairness in Personalized Ads Using Impression Variance Aware Reinforcement Learning](https://mlsanity.com/paper/2306.03293/) (2023)
- [Retiring DeltaDP: New Distribution-Level Metrics for Demographic Parity](https://mlsanity.com/paper/2301.13443/) (2023)
- [On the Maximal Local Disparity of Fairness-Aware Classifiers](https://mlsanity.com/paper/2406.03255/) (2024)
- [A Canonical Data Transformation for Achieving Inter- and Within-group Fairness](https://mlsanity.com/paper/2310.15097/) (2023)
- [Toward Fairness via Maximum Mean Discrepancy Regularization on Logits Space](https://mlsanity.com/paper/2402.13061/) (2024)
- [Balancing Fairness and Accuracy in Data-Restricted Binary Classification](https://mlsanity.com/paper/2403.07724/) (2024)
- [A Critical Review of Predominant Bias in Neural Networks](https://mlsanity.com/paper/2502.11031/) (2025)
- [Toward Calibrated, Fair, and accurate Deepfake Detection](https://mlsanity.com/paper/2606.09881/) (2026)
- [RECAST: Model Reconstruction via Counterfactual-Aware Wasserstein Geometry under Limited Data](https://mlsanity.com/paper/2606.27948/) (2026)

## Abstract (arXiv)

Effective machine learning models can automatically learn useful information from a large quantity of data and provide decisions in a high accuracy. These models may, however, lead to unfair predictions in certain sense among the population groups of interest, where the grouping is based on such sensitive attributes as race and gender. Various fairness definitions, such as demographic parity and equalized odds, were proposed in prior art to ensure that decisions guided by the machine learning models are equitable. Unfortunately, the "fair" model trained with these fairness definitions is threshold sensitive, i.e., the condition of fairness may no longer hold true when tuning the decision threshold. This paper introduces the notion of threshold invariant fairness, which enforces equitable performances across different groups independent of the decision threshold. To achieve this goal, this paper proposes to equalize the risk distributions among the groups via two approximation methods. Experimental results demonstrate that the proposed methodology is effective to alleviate the threshold sensitivity in machine learning models designed to achieve fairness.