Background: As Artificial Intelligence (AI) systems increasingly mediate critical life opportunities—from loan approvals to criminal sentencing—the demand for Explainable AI (XAI) has intensified. However, a significant gap remains between technical methods of explanation and the social requirements of accountability.

Methods: This study employs a critical theoretical analysis, synthesizing literature on algorithmic transparency, legal frameworks regarding disparate impact, and recent empirical data on consumer sentiment and business applications of XAI. We evaluate existing XAI paradigms against the "transparency ideal" to determine their efficacy in ensuring social responsibility.

Results: Our analysis reveals that current XAI techniques often provide "seeing without knowing," offering mathematical approximations that satisfy technical audits but fail to provide actionable understanding for impacted individuals. We find that static transparency mechanisms are insufficient for dynamic learning models and that "one-size-fits-all" explanations often obscure, rather than reveal, bias.

Conclusion: True algorithmic accountability requires moving beyond code availability to "meaningful transparency," which prioritizes the sociological context of decisions. We propose a shift from purely technical explainability to a framework of justifiability, ensuring that AI systems are not only transparent in their function but accountable for their social outcomes.

Explainable AI, Algorithmic Accountability, Transparency, Disparate Impact

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