A scoping review and empirical analysis produce a six-category taxonomy of factors driving AI non-development and abandonment, showing that practical issues like resource limits and organizational dynamics often outweigh ethical concerns in real decisions.
Assuring the Machine Learning Lifecycle : Desiderata , Methods , and Challenges
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The paper proposes the IARC-TS protocol that combines drift monitoring, uncertainty quantification, and stress tests to generate reproducible robustness evidence for industrial time series models mapped to EU AI Act obligations.
A framework using structural causal models simulates parametric drifts to evaluate classifier robustness more realistically than static tests or noise perturbations.
citing papers explorer
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To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systems
A scoping review and empirical analysis produce a six-category taxonomy of factors driving AI non-development and abandonment, showing that practical issues like resource limits and organizational dynamics often outweigh ethical concerns in real decisions.
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Industrial AI Robustness Card for Time Series Models
The paper proposes the IARC-TS protocol that combines drift monitoring, uncertainty quantification, and stress tests to generate reproducible robustness evidence for industrial time series models mapped to EU AI Act obligations.
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Causal Parametric Drift Simulation: A Digital Twin Framework for Classifier Robustness Evaluation
A framework using structural causal models simulates parametric drifts to evaluate classifier robustness more realistically than static tests or noise perturbations.
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