# Fairness Evaluation for Uplift Modeling in the Absence of Ground Truth

arXiv: https://arxiv.org/abs/2403.12069 · HTML: https://mlsanity.com/paper/2403.12069/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 42 references that are in the map.
- First posted on arXiv 12 Feb 2024.

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

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [Fairness in Learning: Classic and Contextual Bandits](https://mlsanity.com/paper/1605.07139/) (2016)
- [A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices](https://mlsanity.com/paper/1807.00787/) (2018)
- [Learning Optimal Fair Policies](https://mlsanity.com/paper/1809.02244/) (2018)
- [A unified survey of treatment effect heterogeneity modeling and uplift modeling](https://mlsanity.com/paper/2007.12769/) (2020)
- [Addressing Marketing Bias in Product Recommendations](https://mlsanity.com/paper/1912.01799/) (2019)
- [Quantifying and Reducing Stereotypes in Word Embeddings](https://mlsanity.com/paper/1606.06121/) (2016)

## Abstract (arXiv)

The acceleration in the adoption of AI-based automated decision-making systems poses a challenge for evaluating the fairness of algorithmic decisions, especially in the absence of ground truth. When designing interventions, uplift modeling is used extensively to identify candidates that are likely to benefit from treatment. However, these models remain particularly susceptible to fairness evaluation due to the lack of ground truth on the outcome measure since a candidate cannot be in both treatment and control simultaneously. In this article, we propose a framework that overcomes the missing ground truth problem by generating surrogates to serve as a proxy for counterfactual labels of uplift modeling campaigns. We then leverage the surrogate ground truth to conduct a more comprehensive binary fairness evaluation. We show how to apply the approach in a comprehensive study from a real-world marketing campaign for promotional offers and demonstrate its enhancement for fairness evaluation.