# Fairness Interventions in Classification: A Study on AI Explainability

arXiv: https://arxiv.org/abs/2407.14766 · HTML: https://mlsanity.com/paper/2407.14766/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 47 references that are in the map.
- First posted on arXiv 20 Jul 2024.

## Builds on (most cited first, 14 of 14)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [A Reductions Approach to Fair Classification](https://mlsanity.com/paper/1803.02453/) (2018)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [On Fairness and Calibration](https://mlsanity.com/paper/1709.02012/) (2017)
- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [FairBatch: Batch Selection for Model Fairness](https://mlsanity.com/paper/2012.01696/) (2020)
- [The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default](https://mlsanity.com/paper/2302.02404/) (2023)
- [Fairness in machine learning: against false positive rate equality as a measure of fairness](https://mlsanity.com/paper/2007.02890/) (2020)
- [Reducing Unintended Bias of ML Models on Tabular and Textual Data](https://mlsanity.com/paper/2108.02662/) (2021)
- [Fairness through Experimentation: Inequality in A/B testing as an approach to responsible design](https://mlsanity.com/paper/2002.05819/) (2020)
- [Measuring vagueness and subjectivity in texts: from symbolic to neural VAGO](https://mlsanity.com/paper/2309.06132/) (2023)

## Abstract (arXiv)

This paper presents a philosophical and experimental study of fairness interventions in AI classification, centered on the explainability and transparency of corrective methods, and on the opposition between two fairness criteria, namely Demographic Parity and Equalized Odds. Our main argument is that even as a gap in Demographic Parity is used to diagnose inequality between groups, Equalized Odds constitutes a more reliable fairness criterion to guide bias correction in classification. To establish this, we present FairDream, a fairness package intended for lay users, whose mechanism increases the model's weights of errors on disadvantaged groups. To justify FairDream's results, we analyze its reweighting algorithm, and we present the results of a benchmark experiment in which we compare FairDream with a distinct in-processing correction method that enforces Demographic Parity more drastically, the GridSearch method. We then propose a normative justification of Equalized Odds, with a discussion of the criterion's limitations. We draw on the structural similarity between FairDream's results and a version of Simpson's paradox to justify conditioning on true labels in counterfactual evaluations of fairness.