Demertzis, K.; Rantos, K.; Magafas, L.; Skianis, C.; Iliadis, L. A Secure and Privacy-Preserving Blockchain-Based XAI-Justice System. Information2023, 14, 477.
Demertzis, K.; Rantos, K.; Magafas, L.; Skianis, C.; Iliadis, L. A Secure and Privacy-Preserving Blockchain-Based XAI-Justice System. Information 2023, 14, 477.
Demertzis, K.; Rantos, K.; Magafas, L.; Skianis, C.; Iliadis, L. A Secure and Privacy-Preserving Blockchain-Based XAI-Justice System. Information2023, 14, 477.
Demertzis, K.; Rantos, K.; Magafas, L.; Skianis, C.; Iliadis, L. A Secure and Privacy-Preserving Blockchain-Based XAI-Justice System. Information 2023, 14, 477.
Abstract
Pursuing "intelligent justice" necessitates an impartial, productive, and technologically driven methodology for judicial determinations. This scholarly composition proposes a framework that harnesses Artificial Intelligence (AI) innovations such as Natural Language Processing (NLP), ChatGPT, ontological alignment, and the semantic web, in conjunction with blockchain and privacy techniques, to examine, deduce, and proffer recommendations for the administration of justice. Specifically, through the integration of blockchain technology, the system affords a secure and transparent infrastructure for the management of legal documentation and transactions while preserving data confidentiality. Privacy approaches, including differential privacy and homomorphic encryption techniques, are further employed to safeguard sensitive data and uphold discretion. The advantages of the suggested framework encompass heightened efficiency and expediency, diminished error propensity, a more uniform approach to judicial determinations, and augmented security and privacy. Additionally, by utilizing explainable AI methodologies, the ethical and legal ramifications of deploying intelligent algorithms and blockchain technologies within the legal domain are scrupulously contemplated, ensuring a secure, efficient, and transparent justice system that concurrently protects sensitive information upholds privacy.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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