Thesis Abstract

Abstract
This thesis explores the impact of artificial intelligence (AI) on copyright law enforcement. In recent years, AI technologies have rapidly advanced and are increasingly being utilized in various sectors, including the legal field. This study aims to analyze how AI is transforming copyright law enforcement practices, the challenges and opportunities it presents, and the implications for copyright holders, law enforcement agencies, and the legal system as a whole. The introduction provides an overview of the research topic, highlighting the growing importance of AI in the field of copyright law enforcement. The background of the study examines the evolution of AI technologies and their applications in the legal domain, particularly in the context of intellectual property rights protection. The problem statement identifies the gaps in existing literature regarding the specific impact of AI on copyright law enforcement and sets the foundation for the research objectives. The objectives of the study include assessing the effectiveness of AI tools in detecting and combating copyright infringement, examining the legal and ethical implications of AI-driven enforcement methods, and proposing recommendations for policymakers and stakeholders. The limitations of the study are acknowledged, including the complexity of copyright law and the evolving nature of AI technologies. The scope of the study focuses on the use of AI in detecting and enforcing copyright violations, with a particular emphasis on machine learning algorithms and digital content analysis tools. The significance of the study lies in its potential to contribute to the understanding of how AI is reshaping copyright law enforcement practices and the need for legal frameworks to adapt to these technological advancements. The structure of the thesis outlines the organization of the research, from the literature review to the methodology, findings, and conclusion. The literature review examines existing research on AI applications in copyright enforcement, highlighting key findings and gaps in knowledge. It explores the benefits and challenges of AI-driven enforcement methods, the role of AI in content identification and infringement detection, and the implications for copyright holders and users. The research methodology section outlines the approach taken in this study, including data collection methods, research design, and data analysis techniques. It discusses the use of case studies, surveys, and interviews to gather insights from copyright experts, technology developers, and legal practitioners. The discussion of findings section presents the results of the study, analyzing the impact of AI on copyright law enforcement, the effectiveness of AI tools in detecting infringement, and the legal and ethical considerations surrounding AI-driven enforcement practices. It also identifies opportunities for future research and policy development in this area. In conclusion, this thesis summarizes the key findings and implications of the research on the impact of artificial intelligence on copyright law enforcement. It highlights the potential benefits of AI technologies in enhancing enforcement efforts, while also emphasizing the need for robust legal frameworks and ethical guidelines to ensure the fair and effective application of AI in copyright protection.

Thesis Overview