Explainable deep learning integrated with decentralized identity systems to combat bias, enhance trust, and ensure fairness in algorithmic governance
Technical Program Manager, Visa Inc. USA.
Research Article
World Journal of Advanced Research and Reviews, 2024, 21(02), 2146-2166
Publication history:
Received on 13 January 2024; revised on 21 February 2024; accepted on 26 February 2024
Abstract:
The growing reliance on artificial intelligence in decision-making processes has intensified debates over bias, fairness, and accountability in algorithmic governance. While deep learning models deliver unprecedented predictive performance, their “black box” nature has undermined transparency and public trust, particularly in high-stakes applications such as finance, healthcare, and digital public services. Explainable AI (XAI) has emerged to address this gap by making model reasoning interpretable, yet explainability alone cannot guarantee fairness without verifiable systems of identity and accountability. This study proposes a framework that integrates explainable deep learning with decentralized identity (DID) systems to combat bias, enhance trust, and ensure equitable governance outcomes. In this framework, explainable deep learning models provide human-understandable insights into algorithmic decisions, enabling stakeholders to evaluate reasoning processes. Meanwhile, decentralized identity systems built on blockchain technologies ensure that individuals retain control over their digital identities, reducing risks of centralized manipulation and exclusion. By linking interpretable models with verifiable identity protocols, algorithmic governance can achieve both transparency and fairness while protecting privacy. The integration enables bias detection and correction at both the model and system levels: interpretable models flag discriminatory features, while decentralized identity guarantees equitable access across diverse populations. Applications in digital voting, welfare distribution, and credit scoring illustrate how the framework strengthens accountability and prevents systemic marginalization. Ultimately, combining explainable deep learning with decentralized identity provides a path toward trustworthy and fair algorithmic governance, where decisions are not only accurate but also transparent, inclusive, and ethically aligned with societal values.
Keywords:
Explainable Deep Learning; Decentralized Identity; Algorithmic Governance; Bias Mitigation; Trustworthy AI; Fairness in Decision-Making
Full text article in PDF:
Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0