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Evidence record 6193 · automatically gathered

CAFP: A Post-Processing Framework for Group Fairness via Counterfactual Model Averaging

Ensuring fairness in machine learning predictions is a critical challenge, especially when models are deployed in sensitive domains such as credit scoring, healthcare, and criminal justice. While many fairness interventions rely on data preprocessing or algorithmic constraints during training, these approaches often require full control over the model architecture and access to protected attribute information, which may not be feasible in real-world systems. In this paper, we propose Counterfact

Record details

Published: 8 April 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Healthcare
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (8 April 2026), “CAFP: A Post-Processing Framework for Group Fairness via Counterfactual Model Averaging,” evidence record 6193, https://ethics.ai/record/6193 (originally published by arXiv).

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