Black-Box Machine Learning versus White-Box Methods: A Literature Review
Keywords:
Black-box models, white-box models, deep learning, explainable AI, interpretable machine learning, machine learning, optimization, literature review, XAIAbstract
The rapid proliferation of machine learning (ML) and deep learning (DL) methods across scientific and industrial domains has intensified the debate between predictive performance and model interpretability. Black-box models, including deep neural networks, ensemble methods, and support vector machines, offer state-of-the-art accuracy but operate with limited transparency. White-box models, encompassing linear programming, interpretable ML, and rule-based systems, prioritize explainability, auditability, and constraint satisfaction. This systematic literature review synthesizes findings from 50 peer-reviewed articles published between 2016 and 2025, retrieved from IEEE Xplore, Scopus, and Web of Science. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, this review compares black-box and white-box approaches across six dimensions: predictive performance, interpretability, computational efficiency, scalability, robustness, and domain applicability. Results reveal a persistent accuracy–interpretability trade-off, an emerging convergence through explainable artificial intelligence (XAI) methods, and growing hybrid architectures that blend both paradigms. This review identifies critical research gaps and provides a structured roadmap for future investigation.
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