Название: Convergence of Blockchain and Explainable Artificial Intelligence: BlockXAI
Автор: Akansha Singh, Krishna Kant Singh
Издательство: River Publishers
Год: 2024
Страниц: 180
Язык: английский
Формат: pdf (true), epub (true)
Размер: 20.4 MB

Explainable AI (XAI) is an upcoming research field in the domain of machine learning. This book aims to provide a detailed description of the topics related to XAI and Blockchain. These two technologies can benefit each other, and the research outcomes will benefit society in multiple ways. Existing AI systems make decisions in a black box manner. Explainable AI delineates how an AI system arrived at a particular decision. It inspects the steps and models that are responsible for making a particular decision. It is an upcoming trend that aims at providing explanations to the AI decisions. Blockchain is emerging as an effective technique for XAI. It enables accessibility to digital ledgers amongst the various AI agents. The AI agents collaborate using consensus and decisions are saved on Blocks. These blocks can be traced back but cannot be changed. Thus, the combination of AI with blockchain provides transparency and visibility to all AI decisions. BlockXAI is also being widely used for improving data security and intelligence. The decisions made are consensus based and decentralized leading to highly efficient AI systems. This book also covers topics that present the convergence of Blockchain with explainable AI and will provide researchers, academics, and industry experts with a complete guide to BlockXAI. This book provides a detailed description of the topics related to XAI and Blockchain.

Machine Intelligence is referred to as “Artificial Intelligence.” Computational intelligence is another name for it. An intelligent agent is a technology that acts in such a manner as to accomplish a pre-determined objective, while also observing its surroundings and learning from its experiences. This is the focus of AI research. Despite Machine Learning’s enormous success, its flaws have grown increasingly apparent as a result of this success. Medical and government advancements in recent years have highlighted the fundamental issues with confidence in these and other key domains. It is a major shortcoming of AI systems because they cannot explain why they made certain decisions. AI systems’ increasing independence and ability to operate on their own means that the decisions and actions they take are becoming more difficult for humans to follow and comprehend. But unless convincing explanations are provided, AI applications will not be able to achieve their potential. To produce Machine Learning approaches that yield more understandable models without compromising prediction accuracy, the explainable AI (XAI) was born. XAI aims to help humans trust, understand, and manage AI systems. XAI research is reviewed in this chapter, although the emphasis is on Artificial Intelligence (AI), which is closely related to XAI. By using blockchain, smart contracts, trusted oracles, and decentralized storage, we also shed light on more reliable XAI.