Explainable AI And Blockchain: Interpretable Machine Learning Models With Cryptographic Security For Critical Decision-Making

Abstract

Explainable Artificial Intelligence (XAI) is crucial for ensuring trust and transparency in AI systems, especially in critical decision-making scenarios (IBM). As AI becomes more integrated into various sectors, understanding how these systems arrive at specific outputs is essential for accountability and ethical considerations (IBM). This essay explores the intersection of XAI and blockchain technology, focusing on the development of interpretable machine learning models with cryptographic security (1 Block XAI: Review of Blockchain for Explainable Artificial Intelligence). Blockchain's decentralized and immutable nature can provide a robust framework for XAI, ensuring that AI decision-making processes are transparent, auditable, and secure (IBM). This study investigates various interpretable machine learning models, such as decision trees and linear regression, and their integration with cryptographic techniques to enhance data privacy and security (Christoph Molnar). By leveraging blockchain's cryptographic features, sensitive data used in AI models can be protected, while XAI techniques provide insights into the model's decision-making process (Nalini). The synergy between XAI and blockchain offers a promising approach for building trustworthy AI systems that can be confidently deployed in high-stakes applications, promoting transparency and accountability in AI-driven decisions (IBM).