SHIELD: Securing Holistic and AI-Inclusive E-Leadership through Decentralized Technologies

DOI

10.2991/978-94-6463-866-0_10How to use a DOI?

Keywords

Smart Contracts; Blockchain; AI-Governance; SLM; Metamask; Web3.0; Remix; Sentiment Analysis; Legal Validation; Text Mining; TF-IDF; Sentence Transformers; Citizen Participation; E-Governance; Smart Governance; Decentralized Applications (DApps)

Abstract

Digital governance aims to enhance the efficiency, transparency, and inclusiveness of public administration through technology. However, current systems often face limitations such as centralized authority, limited civic participation, opaque decision-making, and the absence of intelligent validation frameworks. Existing blockchain-based governance models address specific aspects like document immutability and digital identity but lack comprehensive citizen engagement, custom AI-driven analysis, and legal contextualization. Similarly, various platforms offer automation but fall short in integrating NLP for understanding public sentiment or aligning public demands with constitutional laws. These solutions also typically lack real-time voting capabilities, scalable user interaction, and ethical feedback loops. To bridge these gaps, this research presents SHIELD, a next-generation decentralized governance framework that combines blockchain-based transparency with the interpretive power of advanced Natural Language Processing (NLP). Citizens can submit community demands via a user interface, which are securely recorded using smart contracts. These contracts automate the proposal lifecycle from submission to voting and final prioritization while ensuring tamper-proof audit trails and trust less consensus. The core of SHIELD lies in the custom Decentralised Digital Democracy-AI (D3-AI) module (with customizable legal datasets), which performs advanced NLP tasks including tokenization, lemmatization, TF-IDF vectorization, contextual sentence embeddings, semantic similarity, and Named Entity Recognition (NER). Sentiment analysis is implemented using both DistilBERT and a custom logistic regression model, providing dual insight into emotional tone and statistical features. Furthermore, the module maps proposals to legal articles and regulatory indexes using cosine similarity and generates responses via GPT-2 for clarity and citizen feedback. Data visualizations such as sentiment distributions, network graphs, and calibration curves— assist officials in making transparent, data-driven decisions. Security and administrative integrity are reinforced through Role-Based Access-Control (RBAC), governing user privileges across roles such as citizens, MLAs, and higher public officers (policymakers). A modular backend infrastructure handles session management, secure data orchestration, and system configuration, ensuring resilience, scalability, and operational robustness. By embedding transparency, traceability, and legal validation into every layer of civic participation, this decentralized digital democracy framework offers a transformative blueprint for inclusive, accountable, and AI-augmented governance.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - J. V. K. Kamlesh
AU  - A. V. V. Lokesh Varma
AU  - Sumit Kumar Singh
AU  - Golda Dilip
PY  - 2025
DA  - 2025/10/31
TI  - SHIELD: Securing Holistic and AI-Inclusive E-Leadership through Decentralized Technologies
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 90
EP  - 106
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_10
DO  - 10.2991/978-94-6463-866-0_10
ID  - Kamlesh2025
ER  -