The rapid institutionalization of machine learning systems across scientific, commercial, and public-sector domains has elevated concerns regarding credibility, privacy, robustness, and long-term maintainability. While advances in model architectures and learning paradigms have attracted significant scholarly and industrial attention, foundational challenges related to data quality, system underspecification, privacy leakage, and engineering rigor remain insufficiently integrated into a unified conceptual framework. This article develops a comprehensive, theoretically grounded analysis that synthesizes insights from data cleaning systems, data integration research, differential privacy theory, adversarial machine learning, underspecified model behavior, and classical software engineering methodologies. Drawing strictly on the provided references, the study articulates how data defects propagate through learning pipelines, how underspecification undermines empirical credibility, and how privacy and security threats exploit both data and model artifacts. The methodology adopts a qualitative, analytical synthesis approach, treating established systems and theories as conceptual instruments rather than empirical datasets. Results are presented as a structured descriptive analysis identifying recurring patterns, tensions, and complementarities across the literature. The discussion interprets these findings through the lens of system-level accountability, arguing that credibility in modern machine learning emerges not from isolated technical fixes but from coordinated design principles spanning data preprocessing, algorithm selection, privacy guarantees, verification techniques, and disciplined software development practices. Limitations related to empirical generalization and evolving technological contexts are acknowledged, and future research directions emphasize automated workflow validation, deductive reasoning verification, and institutional governance mechanisms. The article concludes that a credible machine learning system must be understood as an engineered socio-technical artifact, whose reliability depends equally on data hygiene, theoretical guarantees, and sustainable engineering processes.

Machine learning credibility, data quality, differential privacy

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