Название: Data Protection: The Wake of AI and Machine Learning
Автор: Chaminda Hewage, Lasith Yasakethu, Dushmantha Nalin K. Jayakody
Издательство: Springer
Год: 2024
Страниц: 309
Язык: английский
Формат: pdf (true), epub
Размер: 26.9 MB

This book provides a thorough and unique overview of the challenges, opportunities and solutions related with data protection in the age of Artificial Intelligence (AI) and Machine Learning (ML) technologies. It investigates the interface of data protection and new technologies, emphasising the growing need to safeguard personal and confidential data from unauthorised access and change. The authors emphasize the crucial need of strong data protection regulations, focusing on the consequences of AI and ML breakthroughs for privacy and individual rights. This book emphasizes the multifarious aspect of data protection, which goes beyond technological solutions to include ethical, legislative and societal factors.

The core concept behind AI is to simulate human-like intelligence in machines, allowing them to analyze complex data, recognize patterns, and make informed decisions. This field has evolved significantly, from early symbolic AI systems that relied on predefined rules to modern AI systems that leverage ML algorithms for data-driven learning. ML algorithms play a pivotal role in AI development by enabling machines to learn from data. These algorithms are designed to extract meaningful insights and patterns from large datasets, which are then used to make predictions or decisions.

This book explores into the complexity of data protection in the age of AI and ML. It investigates how massive volumes of personal and sensitive data are utilized to train and develop AI models, demanding novel privacy-preserving strategies such as anonymization, differential privacy and federated learning. The duties and responsibilities of engineers, policy makers and ethicists in minimizing algorithmic bias and ensuring ethical AI use are carefully defined. Key developments, such as the influence of the European Union's General Data Protection Regulation (GDPR) and the EU AI Act on data protection procedures, are reviewed critically. This investigation focusses not only on the tactics used, but also on the problems and successes in creating a secure and ethical AI ecosystem. This book provides a comprehensive overview of the efforts to integrate data protection into AI innovation, including valuable perspectives on the effectiveness of these measures and the ongoing adjustments required to address the fluid nature of privacy concerns.

This book is a helpful resource for upper-undergraduate and graduate Computer Science students, as well as others interested in cybersecurity and data protection. Researchers in AI, ML, and data privacy as well as data protection officers, politicians, lawmakers and decision-makers will find this book useful as a reference.






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