# Fairness in Risk Assessment Instruments: Post-Processing to Achieve Counterfactual Equalized Odds

arXiv: https://arxiv.org/abs/2009.02841 · HTML: https://mlsanity.com/paper/2009.02841/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2020.

## Built on by (most cited first, 30 of 40)

- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [Causal Conceptions of Fairness and their Consequences](https://mlsanity.com/paper/2207.05302/) (2022)
- [Fairness Reprogramming](https://mlsanity.com/paper/2209.10222/) (2022)
- [Adaptive Sampling Strategies to Construct Equitable Training Datasets](https://mlsanity.com/paper/2202.01327/) (2022)
- [What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective](https://mlsanity.com/paper/2206.04101/) (2022)
- [A Review of the Role of Causality in Developing Trustworthy AI Systems](https://mlsanity.com/paper/2302.06975/) (2023)
- [Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification](https://mlsanity.com/paper/2101.07361/) (2021)
- [FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomes](https://mlsanity.com/paper/2109.00173/) (2021)
- [The Pursuit of Fairness in Artificial Intelligence Models: A Survey](https://mlsanity.com/paper/2403.17333/) (2024)
- [Fair When Trained, Unfair When Deployed: Observable Fairness Measures are Unstable in Performative Prediction Settings](https://mlsanity.com/paper/2202.05049/) (2022)
- [On the Fairness of Machine-Assisted Human Decisions](https://mlsanity.com/paper/2110.15310/) (2021)
- [FAIR-FATE: Fair Federated Learning with Momentum](https://mlsanity.com/paper/2209.13678/) (2022)
- [Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding](https://mlsanity.com/paper/2212.09844/) (2022)
- [Fair Wasserstein Coresets](https://mlsanity.com/paper/2311.05436/) (2023)
- [The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning](https://mlsanity.com/paper/2410.09600/) (2024)
- [Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors](https://mlsanity.com/paper/2301.08987/) (2023)
- [Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools](https://mlsanity.com/paper/2309.17337/) (2023)
- [Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning](https://mlsanity.com/paper/2306.03625/) (2023)
- [Auditing Fairness under Unobserved Confounding](https://mlsanity.com/paper/2403.14713/) (2024)
- [Doubly Robust Counterfactual Classification](https://mlsanity.com/paper/2301.06199/) (2023)
- [Multi-dimensional Fair Federated Learning](https://mlsanity.com/paper/2312.05551/) (2023)
- [Procedural Fairness Through Decoupling Objectionable Data Generating Components](https://mlsanity.com/paper/2311.14688/) (2023)
- [Hard Choices and Hard Limits for Artificial Intelligence](https://mlsanity.com/paper/2105.07852/) (2021)
- [Counterfactual Reasoning for Bias Evaluation and Detection in a Fairness under Unawareness setting](https://mlsanity.com/paper/2302.08204/) (2023)
- [Specification Overfitting in Artificial Intelligence](https://mlsanity.com/paper/2403.08425/) (2024)
- [Counterfactual Fairness through Transforming Data Orthogonal to Bias](https://mlsanity.com/paper/2403.17852/) (2024)
- [Increasing Fairness in Predictions Using Bias Parity Score Based Loss Function Regularization](https://mlsanity.com/paper/2111.03638/) (2021)
- [Principled Diverse Counterfactuals in Multilinear Models](https://mlsanity.com/paper/2201.06467/) (2022)
- [Migrate Demographic Group For Fair GNNs](https://mlsanity.com/paper/2306.04212/) (2023)
- [Mind the Gap: Optimal and Equitable Encouragement Policies](https://mlsanity.com/paper/2309.07176/) (2023)

## Abstract (arXiv)

In domains such as criminal justice, medicine, and social welfare, decision makers increasingly have access to algorithmic Risk Assessment Instruments (RAIs). RAIs estimate the risk of an adverse outcome such as recidivism or child neglect, potentially informing high-stakes decisions such as whether to release a defendant on bail or initiate a child welfare investigation. It is important to ensure that RAIs are fair, so that the benefits and harms of such decisions are equitably distributed. The most widely used algorithmic fairness criteria are formulated with respect to observable outcomes, such as whether a person actually recidivates, but these criteria are misleading when applied to RAIs. Since RAIs are intended to inform interventions that can reduce risk, the prediction itself affects the downstream outcome. Recent work has argued that fairness criteria for RAIs should instead utilize potential outcomes, i.e. the outcomes that would occur in the absence of an appropriate intervention. However, no methods currently exist to satisfy such fairness criteria. In this paper, we target one such criterion, counterfactual equalized odds. We develop a post-processed predictor that is estimated via doubly robust estimators, extending and adapting previous post-processing approaches to the counterfactual setting. We also provide doubly robust estimators of the risk and fairness properties of arbitrary fixed post-processed predictors. Our predictor converges to an optimal fair predictor at fast rates. We illustrate properties of our method and show that it performs well on both simulated and real data.