Security and privacy aspects in intelligence systems through blockchain and explainable AI
- Title
- Security and privacy aspects in intelligence systems through blockchain and explainable AI
- Creator
- Thiruthuvanathan M.M.; Pradeep Kumar K.; Nasar N.; K.K S.; Joseph P.M.
- Description
- Explainable AI (XAI) is a method of creating artificial intelligence (AI) systems that are transparent and understandable to humans. By allowing people to understand how the system arrived at its conclusions or suggestions, XAI systems strive to make AI more accountable, trustworthy, and ethical. Responsibility, trust, ethics, regulation, and innovation are some of the societal ramifications of XAI. By making AI systems more transparent, XAI fosters accountability. This means that consumers will be able to understand how the system made its decisions and hold it accountable if something goes wrong. By making the decision-making process more transparent, XAI fosters trust between people and AI systems. This boosts user trust in the system and encourages wider adoption of AI technologies. It also contributes to the ethical design of AI systems by making the decision-making process public in order to uncover and mitigate biases and other ethical issues that may occur in AI systems. It aids regulators and policymakers in understanding and regulating AI systems. XAI gives insight into how AI systems operate, which can assist regulators in developing laws that promote ethical and responsible AI use. Because XAI can help developers better and innovate new systems by making it easier for them to design new AI systems and by providing insights into how AI systems work. The proposed chapter will focus on important aspects of algorithmic bias and changing notions of privacy in XAI, which will necessitate the need for AI systems that can adapt accountability, trust, ethics, and compliance with regulations, as well as produce better innovation that can benefit humanity. More openness, greater control over personal data, new types of data privacy, and newer privacy networks are all required. To address algorithmic bias in XAI, it is critical to build the system so that it is aware of the possibility of bias and actively mitigates it. This can involve employing diverse and representative data, inspecting the system for unwanted features, offering detailed explanations, and incorporating a wide range of stakeholders in the system's development and deployment. The envisaged report provides a framework that combines XAI and blockchain to provide a secure and transparent way to store and track the provenance of data used by XAI systems, validate the performance of AI models stored on the blockchain on decentralized systems so that the models are stored and executed on a distributed network of nodes rather than a centralized server, and create a token-based economy that encourages data sharing and AI development. Tokens can be used to compensate individuals and organizations who contribute data or algorithms to the blockchain or who employ AI models stored on the blockchain. Overall, the combination of XAI and blockchain can lead to more trustworthy, transparent, and decentralized AI systems. This approach can have a significant impact on various industries such as finance, healthcare, and supply chain management by increasing efficiency, reducing costs, and improving data privacy and security. 2024 Elsevier Inc. All rights reserved.
- Source
- XAI Based Intelligent Systems for Society 5.0, pp. 365-400.
- Date
- 2023-01-01
- Publisher
- Elsevier
- Subject
- AI security; Blockchain; Explainable AI; Healthcare analytics; Intelligence systems
- Coverage
- Thiruthuvanathan M.M., Department of Computer Science Engineering, School of Engineering and Technology, CHRIST University, Kengeri Campus, Karnataka, Bangalore, India; Pradeep Kumar K., Department of Computer Science Engineering, School of Engineering and Technology, CHRIST University, Kengeri Campus, Karnataka, Bangalore, India; Nasar N., Department of Computer Science Engineering, School of Engineering and Technology, CHRIST University, Kengeri Campus, Karnataka, Bangalore, India; K.K S., Department of Computer Science Engineering, School of Engineering and Technology, CHRIST University, Kengeri Campus, Karnataka, Bangalore, India; Joseph P.M., Department of Computer Science, Modern College of Business and Science, Muscat, Oman
- Rights
- Restricted Access
- Relation
- ISBN: 978-032395315-3; 978-032395784-7
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Thiruthuvanathan M.M.; Pradeep Kumar K.; Nasar N.; K.K S.; Joseph P.M., “Security and privacy aspects in intelligence systems through blockchain and explainable AI,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/18376.