# Counterfactual-Faithful Quantization aims to keep algorithmic recourse stable after compression

_Tuesday, August 4, 2026 at 12:00 AM EDT · AI, Science · Latest · Tier 2 — Notable_

An arXiv machine learning paper studies how model quantization can break algorithmic recourse even when predictive accuracy is preserved. Small actionable changes that flip a full-precision model may fail after quantization or require a larger intervention.

The authors define counterfactual sensitivity under quantization with two metrics: Validity Drop, the fraction of full-precision recourse actions that no longer achieve the target after quantization, and Counterfactual Recourse Gap, the increase in minimal recourse cost under the quantized model.

They propose Counterfactual-Faithful Quantization, a quantization-aware training method that jointly learns quantizer parameters and mixed-precision bit allocation while preserving the target prediction at teacher-generated recourse points. CFQ works with standard LSQ/PACT-style quantizers and mixed-precision policies and can also run as training-free calibration for post-training quantization.

On Adult, German Credit, and COMPAS, standard quantization-aware training and mixed-precision baselines can keep accuracy while degrading recourse stability. At matched accuracy and bit budget, CFQ reduces Validity Drop and Counterfactual Recourse Gap; on Adult, the paper reports VD/CRG falling from 0.121/0.162 for an accuracy-centric mixed-precision baseline to 0.061/0.071.

## Sources

- [arXiv](https://arxiv.org/abs/2605.17160)

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Retrieved: 2026-08-04T15:48:29.798Z
Publisher: Tech & Business (techandbusiness.org)