Background: As Artificial Intelligence systems increasingly mediate high-stakes decisions in sectors such as human resources, finance, and security, the "Black Box" nature of complex algorithms has precipitated a crisis of trust. While performance metrics for these models continue to improve, the opacity of their decision-making processes hinders broad organizational adoption. Methods: This study employs an integrative theoretical analysis to examine the relationship between Explainable AI (XAI) methodologies and human trust. We synthesize insights from recent technical literature regarding the "disagreement problem" in feature importance estimation and juxtapose them with behavioral studies on user perception of algorithmic hiring and corporate transparency frameworks. Results: The analysis reveals that technical explainability does not automatically translate to functional transparency. We identify that post-hoc interpretability methods often generate "unjustified counterfactuals," creating a false sense of security. Furthermore, evidence suggests that in high-risk domains like recruitment, the dissonance between different explanation models significantly degrades user confidence. Conclusion: Fostering genuine trust requires a dual approach: advancing technical consistency in XAI outputs to resolve the disagreement problem and aligning explanation interfaces with the cognitive workflows of non-technical stakeholders. We propose a tiered transparency framework that segments interpretability based on stakeholder risk profiles.

Explainable AI, Algorithmic Trust, Machine Learning Transparency, Disagreement Problem

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