A Theory of AI-Driven Trust and Truthfulness in Large-Scale Data Systems
Abstract
The rapid expansion of large-scale data systems has elevated the need for mechanisms that ensure trust, transparency, and truthfulness in AI-driven environments. As organizations increasingly rely on automated decision-making, the integrity of data pipelines and model behaviors has become a central concern. This paper proposes a theoretical foundation for understanding how AI can create, reinforce, or compromise trust and truthfulness in complex data ecosystems. The framework integrates concepts from algorithmic accountability, data provenance, bias detection, uncertainty estimation, and explainable AI to examine how trust is formed between users, systems, and the data that drives them. The theory outlines how AI models can validate information authenticity, detect manipulation, correct inconsistencies, and enhance reliability through self-auditing and continuous learning. Additionally, it explores the role of ethical AI governance, verifiable data lineage, and trust-aware architectures in sustaining truthfulness at scale. By unifying technical, cognitive, and ethical dimensions, the paper establishes a holistic theoretical model that guides the design of transparent, trustworthy, and ethically aligned large-scale data systems powered by AI
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