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Global Multidisciplinary Journal

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Beyond the Black Box: Bridging the Gap Between Technical Explainability and Social Accountability in Algorithmic Decision-Making

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Yashika Vipulbhai Shankheshwaria 1 ,
4 Department of Computer Science and Engineering Parul University Vadodara, Gujarat, India

Abstract

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.

Keywords

Explainable AI, Algorithmic Accountability, Transparency, Disparate Impact

References

📄 Yashika Vipulbhai Shankheshwaria, & Dip Bharatbhai Patel. (2025). Explainable AI in Machine Learning: Building Transparent Models for Business Applications. Frontiers in Emerging Artificial Intelligence and Machine Learning, 2(08), 08–15. https://doi.org/10.37547/feaiml/Volume02Issue08-02
📄 Partnership on AI (2024-04-22).
📄 M Ananny, K Crawford (2018). Seeing without knowing: limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society, 20, 973-989.
📄 J Angwin, J Larson, S Mattu, L Kirchner, V Bellamy, R K Chen, P Y Dhurandhar, A Hind, M Hoffman, S C (2016). Machine Bias. ProPublica.
📄 Guidelines on Automated Individual Decision-Making and Profiling for the Purposes of Regulation 2016/679.
📄 One explanation does not fit all: a toolkit and taxonomy of AI explainability techniques (2023). J. Artif. Intell. Res, 68, 213-228.
📄 S Baker, W Xiang (2023). Explainability and social responsibility in AI systems.
📄 Haan, K. (July 2023), ‘Artificial Intelligence and Consumer Sentiment’, Forbes.
📄 Pew Research Center (2023), ‘Public Awareness of Artificial Intelligence in Everyday Activities’.
📄 S Barocas, A D Selbst (2016). Big data's disparate impact. Calif. L. Rev, 104.

How to Cite

Yashika Vipulbhai Shankheshwaria. (2025). Beyond the Black Box: Bridging the Gap Between Technical Explainability and Social Accountability in Algorithmic Decision-Making. Global Multidisciplinary Journal, 4(11), 45-50. https://www.grpublishing.org/journals/index.php/gmj/article/view/204

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