Improving Model Safety by Targeted Error Correction

2026-05-04

AI Summary

This work addresses the high-risk, non-human misclassifications arising from machine learning models in safety-critical applications by proposing a post-hoc correction framework based on two gradient-boosted decision tree (GBDT) classifiers, without requiring retraining of the primary model. The method identifies high-risk errors and applies conservative corrections, achieving substantial improvements in system safety while incurring minimal inference overhead (<1.85%). Evaluated on the ISIC and SICAPv2 datasets, the approach reduces high-risk errors by 34.1% and 12.57%, respectively, and attains superclass diagnostic safety rates of 90.41% and 92.13%, significantly outperforming the baseline based on maximum class probability.

Abstract

The widespread adoption of machine learning in critical applications demands techniques to mitigate high-consequence errors. Our method utilizes a dual-classifier GBDT pipeline to distinguish routine human-like errors from high-risk non-human misclassifications. Evaluated across three domains, animal breed classification, skin lesion diagnosis (ISIC 2018), and prostate histopathology (SICAPv2), our framework demonstrates robust safety improvements. To address real-world deployment concerns, our results confirm the pipeline introduces negligible inference latency (1.60% overhead for the animal dataset, 1.84% for ISIC, and 1.70% for SICAPv2) while outperforming traditional Maximum Class Probability (MCP) baselines in correction precision. Our conservative correction strategy successfully reduced dangerous non-human errors by 34.1% in ISIC and 12.57% in SICAPv2, improving super-class diagnostic safety to 90.41% and 92.13% respectively. This proves that safety-critical reliability can be substantially enhanced post-hoc without expensive model retraining. keywords: Error Analysis, Post-hoc Correction, Trustworthy AI.

Problem

• Error Analysis
• Post-hoc Correction
• Trustworthy AI
• Model Safety
• High-consequence Errors

Innovation

• Post-hoc Correction
• Dual-classifier GBDT
• High-risk Error Mitigation
• Trustworthy AI
• Model Safety