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Evidence record 16956 · automatically gathered

Local Violation Certification for Linear Predict-Then-Optimize Pipelines

Data-driven decision pipelines combining predictive machine learning models with downstream optimization software are increasingly used to make high-stakes operational decisions. Certifying the safety, fairness, and reliability of these decisions is essential, yet traditional scenario generation methods rely on repeated random testing, which becomes computationally prohibitive when failure events are rare and offers little insight into why failures occur. We present a framework for local violati

Record details

Published: 5 August 2026
Source: arXiv fairness query
Category: Research
Topics: Bias & fairness
Retrieved: 6 August 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.

Shared topic: Bias & fairness

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How to cite this record

ethics.ai (5 August 2026), “Local Violation Certification for Linear Predict-Then-Optimize Pipelines,” evidence record 16956, https://ethics.ai/record/16956 (originally published by arXiv fairness query).

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