Background: As Artificial Intelligence (AI) systems increasingly mediate critical decisions in healthcare, finance, and governance, the "black box" nature of complex algorithms poses significant ethical and operational risks. The opacity of deep learning models creates a trust deficit that hinders adoption and obscures algorithmic bias.

Methods: This study employs a systematic conceptual synthesis to integrate technical Explainable AI (XAI) methodologies with the broader organizational mandate of Corporate Digital Responsibility (CDR). We analyze current XAI taxonomies, bias mitigation strategies, and trust maturity models to propose a unified "Trust-Explainability-Responsibility" (TER) framework.

Results: Our analysis demonstrates that technical explainability alone is insufficient for establishing trust. The TER framework establishes that transparency must be tiered according to stakeholder needs—providing local interpretability for end-users and global interpretability for auditors. Furthermore, we identify that integrating blockchain-based dynamic consent mechanisms significantly enhances data provenance and perceived fairness.

Conclusion: We conclude that operationalizing responsible AI requires a shift from purely accuracy-driven metrics to a holistic evaluation of model behavior. The proposed framework offers a roadmap for organizations to align algorithmic performance with ethical norms, ensuring that AI systems remain transparent, fair, and accountable.

Explainable AI (XAI), Corporate Digital Responsibility, Algorithmic Bias, Machine Learning Transparency

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