Satu Elisa Schaeffer – författare

The rapid advancement of Generative AI (GenAI) is revolutionizing and transforming a wide range of fields, with a notable impact on digital media and information dissemination. Digital platforms, including social media networks, have already grappled with the widespread proliferation of fake news at an increasing intensity and are now facing an even bigger threat from machine-generated and machine-boosted fake news. With the increasing growth and sophistication of GenAI models for malicious ends, fake news is spreading rapidly, finding a fertile ground in digital media for largely unchecked dissemination of incorrect and misleading information. The scalability and diversity of current and emerging GenAI techniques present both opportunities and challenges in the field of fake news. On the one hand, the ability to create highly realistic and persuasive content in large quantities with minimal effort, in real-time, makes GenAI a potent tool for spreading fake news and misinformation. On the other hand, those same capabilities can be harnessed to employ GenAI models to detect fake news and, therefore, hinder the spread. In this context, this book examines how GenAI techniques are contributing to combat fake news in digital media. A particular major challenge in combating fake news with GenAI is the explainability of the models: why a specific piece of news is determined to be fake or genuine. Tracing the origins of a specific piece of generated content-whether it stems from accurate or misleading data-requires extracting, interpreting, and representing in a legible manner vast parameter spaces and training data sources. This complexity of GenAI explainability makes it difficult to hold creators of fake news accountable and to ensure transparency in the content creation process. The authors focus on the positive potential of GenAI and provide an overview of how it can be leveraged to combat fake news across diverse digital media platforms.The book is structured around the following key topics: An overview of GenAI and its relationship with fake news How GenAI can be utilized to fight the spread of fake news Ensuring that the news we encounter on digital media platforms is not misleading or false Methods for verifying the authenticity of news sources and tracing the origins of information to ensure its credibility Practical insights into technological advancements and solutions related to the detection and analysis of fake news Finally, this book serves as a platform for the community to share innovative research on the use of GenAI in detecting and mitigating the impact of false information across digital platforms. Readers will explore how GenAI can revolutionize fact-checking, automate the identification of misleading content, and provide adaptive solutions to prevent the spread of fake news.

The rapid advancement of Generative AI (GenAI) is revolutionizing and transforming a wide range of fields, with a notable impact on digital media and information dissemination. Digital platforms, including social media networks, have already grappled with the widespread proliferation of fake news at an increasing intensity and are now facing an even bigger threat from machine-generated and machine-boosted fake news. With the increasing growth and sophistication of GenAI models for malicious ends, fake news is spreading rapidly, finding a fertile ground in digital media for largely unchecked dissemination of incorrect and misleading information. The scalability and diversity of current and emerging GenAI techniques present both opportunities and challenges in the field of fake news. On the one hand, the ability to create highly realistic and persuasive content in large quantities with minimal effort, in real-time, makes GenAI a potent tool for spreading fake news and misinformation. On the other hand, those same capabilities can be harnessed to employ GenAI models to detect fake news and, therefore, hinder the spread. In this context, this book examines how GenAI techniques are contributing to combat fake news in digital media. A particular major challenge in combating fake news with GenAI is the explainability of the models: why a specific piece of news is determined to be fake or genuine. Tracing the origins of a specific piece of generated content-whether it stems from accurate or misleading data-requires extracting, interpreting, and representing in a legible manner vast parameter spaces and training data sources. This complexity of GenAI explainability makes it difficult to hold creators of fake news accountable and to ensure transparency in the content creation process. The authors focus on the positive potential of GenAI and provide an overview of how it can be leveraged to combat fake news across diverse digital media platforms.The book is structured around the following key topics: An overview of GenAI and its relationship with fake news How GenAI can be utilized to fight the spread of fake news Ensuring that the news we encounter on digital media platforms is not misleading or false Methods for verifying the authenticity of news sources and tracing the origins of information to ensure its credibility Practical insights into technological advancements and solutions related to the detection and analysis of fake news Finally, this book serves as a platform for the community to share innovative research on the use of GenAI in detecting and mitigating the impact of false information across digital platforms. Readers will explore how GenAI can revolutionize fact-checking, automate the identification of misleading content, and provide adaptive solutions to prevent the spread of fake news.

The rapid advancement of Generative AI (GenAI) is revolutionizing and transforming a wide range of fields, with a notable impact on digital media and information dissemination. Digital platforms, including social media networks, have already grappled with the widespread proliferation of fake news at an increasing intensity and are now facing an even bigger threat from machine-generated and machine-boosted fake news. With the increasing growth and sophistication of GenAI models for malicious ends, fake news is spreading rapidly, finding a fertile ground in digital media for largely unchecked dissemination of incorrect and misleading information. The scalability and diversity of current and emerging GenAI techniques present both opportunities and challenges in the field of fake news. On the one hand, the ability to create highly realistic and persuasive content in large quantities with minimal effort, in real-time, makes GenAI a potent tool for spreading fake news and misinformation. On the other hand, those same capabilities can be harnessed to employ GenAI models to detect fake news and, therefore, hinder the spread. In this context, this book examines how GenAI techniques are contributing to combat fake news in digital media. A particular major challenge in combating fake news with GenAI is the explainability of the models: why a specific piece of news is determined to be fake or genuine. Tracing the origins of a specific piece of generated content—whether it stems from accurate or misleading data—requires extracting, interpreting, and representing in a legible manner vast parameter spaces and training data sources. This complexity of GenAI explainability makes it difficult to hold creators of fake news accountable and to ensure transparency in the content creation process. The authors focus on the positive potential of GenAI and provide an overview of how it can be leveraged to combat fake news across diverse digital media platforms.The book is structured around the following key topics:An overview of GenAI and its relationship with fake newsHow GenAI can be utilized to fight the spread of fake newsEnsuring that the news we encounter on digital media platforms is not misleading or falseMethods for verifying the authenticity of news sources and tracing the origins of information to ensure its credibilityPractical insights into technological advancements and solutions related to the detection and analysis of fake newsFinally, this book serves as a platform for the community to share innovative research on the use of GenAI in detecting and mitigating the impact of false information across digital platforms. Readers will explore how GenAI can revolutionize fact-checking, automate the identification of misleading content, and provide adaptive solutions to prevent the spread of fake news.

This book explores the transformative impact of generative artificial intelligence (GenAI) on teaching and learning, examining how recent advancements in GenAI are revolutionizing educational practices across disciplines.

This book explores the transformative impact of generative artificial intelligence (GenAI) on teaching and learning, examining how recent advancements in GenAI are revolutionizing educational practices across disciplines. The book is organized into three parts: an overview of GenAI in education, its application in diverse educational contexts, and future perspectives on how educators and GenAI can interface.The first part addresses the pressing concerns within the educational landscape, both the bridges GenAI allows us to build and the remaining as well as the emerging gaps. The middle part explores specific academic disciplines, such as history, sports medicine, mathematics, engineering, and the humanities, dissecting the influence of GenAI on each. The final part looks ahead, discussing the ethical implications, the evolving role of prompting, and innovative frameworks for personalized learning.By presenting a balanced view of the opportunities that are now within reach through GenAI and the challenges such leaps pose to the way we learn and teach, this book allows interested educators to learn from the early-adopting contributors to fruitfully and responsibly integrate such technologies into their pedagogical practices. It serves as a resource for anyone interested in the future of educational practices and research of education, offering insights that can spark further exploration and discussion within the academic community and educational policy makers.