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Evidence record 3059 · automatically gathered

Trustworthy Machine Learning through the Lens of Combinatorial Optimization: Survey and Research Perspectives

Modern machine learning (ML) increasingly relies on complex models whose behavior is difficult to characterize beyond empirical performance metrics. Across a wide range of tasks, including prediction, generation, and decision-making, models with similar empirical performance can exhibit markedly different properties in terms of their transparency, interpretability, robustness, fairness, privacy, and certifiability. This survey highlights how optimization- and certification-oriented reasoning can

Record details

Published: 8 July 2026
Source: arXiv fairness query
Category: Research
Topics: Bias & fairness · Safety & alignment · Privacy · Transparency
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.

Shared topic: Bias & fairness · Shared topic: Safety & alignment

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How to cite this record

ethics.ai (8 July 2026), “Trustworthy Machine Learning through the Lens of Combinatorial Optimization: Survey and Research Perspectives,” evidence record 3059, https://ethics.ai/record/3059 (originally published by arXiv fairness query).

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