Context: As Artificial Intelligence (AI) systems increasingly automate high-stakes decisions in healthcare, employment, and law, the opacity of "black box" models presents significant ethical and practical challenges. While deep learning offers superior predictive performance, its lack of transparency can obscure algorithmic bias and degrade user trust.

Objective: This article critiques the current landscape of Explainable AI (XAI) and fairness mechanisms, proposing an integrative framework that aligns algorithmic complexity with human cognitive limitations and ethical standards.

Methodology: We conducted a comprehensive synthesis of literature regarding XAI visualization, cognitive load theory, algorithmic bias detection, and open-source fairness toolkits. The analysis focuses on the intersection of technical interpretability (e.g., Concept Bottleneck Models, TCAV) and human-computer interaction (HCI).

Results: The review identifies a critical gap between mathematical explainability and user comprehension. High-fidelity explanations often increase cognitive load, paradoxically reducing user confidence. Furthermore, while technical bias mitigation tools exist, they are often ill-equipped to handle contextual nuances in domains like nephrology and recruitment.

Conclusion: We argue that realistic individual recourse and concept-based explanations are superior to simple feature attribution for fostering trust. Future AI development must prioritize "Open Social Innovation" and iterative usability testing to ensure systems are not only accurate but also intelligible and legally robust under frameworks like the EU’s data protection initiatives.

Explainable AI, Algorithmic Bias, Digital Ethics, Cognitive Load

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