Trustworthy and Uncertainty-Aware Machine Learning for Large-Scale Data
A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "AI-Driven Innovations".
Deadline for manuscript submissions: 30 September 2026 | Viewed by 131
Special Issue Editor
Interests: trustworthy machine learning; uncertainty-aware learning; conformal prediction; distribution-free inference; uncertainty quantification; reliable artificial intelligence; robust machine learning; model calibration; explainable AI; interpretable machine learning; big data analytics; large-scale data mining; learning under distribution shift; decision-making under uncertainty
Special Issue Information
Dear Colleagues,
With the rapid deployment of machine learning systems in high-stakes, real-world applications, ensuring the trustworthiness and reliability of model predictions has become a central challenge. Traditional machine learning approaches often focus on predictive accuracy while overlooking uncertainty, robustness, and reliability—factors that are critical when models are applied to large-scale, heterogeneous, and dynamically evolving data.
This Special Issue aims to bring together recent advances in trustworthy and uncertainty-aware machine learning, with a particular focus on large-scale data analytics. Topics of interest include, but are not limited to, methods for uncertainty quantification, distribution-free prediction, conformal prediction, robustness, and reliability in modern machine learning systems. Both theoretical developments and practical applications are welcome.
The goal of this Special Issue is to provide a comprehensive overview of state-of-the-art methods and emerging challenges in building machine learning models that are not only accurate but also reliable, interpretable, and trustworthy when deployed at scale.
Topics of Interest
Topics include, but are not limited to, the following:
- Uncertainty quantification in machine learning;
- Conformal prediction and distribution-free inference;
- Trustworthy and reliable machine learning systems;
- Robust learning under data distribution shifts;
- Calibration and confidence estimation in large-scale models;
- Explainability and interpretability for trustworthy AI;
- Machine learning for big data analytics and data mining;
- Scalable algorithms for uncertainty-aware learning;
- Decision-making under uncertainty;
- Applications in high-stakes domains (e.g., healthcare, finance, industry, smart systems).
Dr. Meng Yang
Guest Editor
Manuscript Submission Information
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Keywords
- trustworthy machine learning
- uncertainty-aware learning
- conformal prediction
- distribution-free inference
- uncertainty quantification
- reliable artificial intelligence
- robust machine learning
- model calibration
- explainable AI
- interpretable machine learning
- big data analytics
- large-scale data mining
- learning under distribution shift
- decision-making under uncertainty
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