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Scientific, political and bureaucratic elites use epistemic practices like "big data analysis" and "web scraping" to create representations of the citizenry and to legitimize policymaking. I develop the concept of "demos scraping" for these practices of gaining information about citizens (the "demos") through automated analysis of digital trace data which are re-purposed for political means. This article critically engages with the discourse advocating demos scraping and provides a conceptual analysis of its democratic implications. It engages with the promise of demos scraping advocates to reduce the gap between political elites and citizens and highlights how demos scraping is presented as a superior form of accessing the "will of the people" and to increase democratic legitimacy. This leads me to critically discuss the implications of demos scraping for political representation and participation. In its current form, demos scraping is technocratic and de-politicizing; and the larger political and economic context in which it takes place makes it unlikely that it will reduce the gap between elites and citizens. From the analytic perspective of a post-democratic turn, demos scraping is an attempt of late modern and digitalized societies to address the democratic paradox of increasing citizen expectations coupled with a deep legitimation crisis.

Addressing the issue of Cyberbullying to find effective solutions is a challenge for the web mining community, particularly within the realm of social media. In this context AI is as a valuable tool in combating the diverse manifestations of Cyberbullying on the internet and social networks

Addressing the issue of Cyberbullying to find effective solutions is a challenge for the web mining community, particularly within the realm of social media. In this context AI is as a valuable tool in combating the diverse manifestations of Cyberbullying on the internet and social networks.

Digital forensics is the science of detecting evidence from digital media like a computer, smart phone, server, or network. It provides the forensic team with the most beneficial methods to solve confused digital-related cases. AI and blockchain can be applied to solve online predatory chat cases and photo forensics cases, provide network service evidence, custody of digital files in forensic medicine, and identify roots of data scavenging. The increased use of PCs and extensive use of internet access, has meant easy availability of hacking tools. Over the past two decades, improvements in the information technology landscape have made the collection, preservation, and analysis of digital evidence extremely important. The traditional tools for solving cybercrimes and preparing court cases are making investigations difficult. We can use AI and blockchain design frameworks to make the digital forensic process efficient and straightforward. AI features help determine the contents of a picture, detect spam email messages and recognize swatches of hard drives that could contain suspicious files. Blockchain-based lawful evidence management schemes can supervise the entire evidence flow of all of the court data. This book can provide a wide-ranging overview of how AI and blockchain can be used to solve problems in digital forensics using advanced tools and applications available on the market

Cover -- Half Title -- Untitled -- Series -- Title -- Copyright -- Table of Contents -- Preface -- Acknowledgment -- List of Contributors -- List of Figures -- List of Tables -- List of Abbreviations -- 1 Digital Forensics Meets AI: A Game-changer for the 4th Industrial Revolution -- 1.1 Introduction -- 1.2 Digital Forensics -- 1.2.1 Growing need for digital forensics -- 1.2.2 Process of digital forensics -- 1.2.3 Advantages offered and limitations confronted by digital forensics -- 1.3 AI and Digital Forensics -- 1.3.1 Contribution of AI in the realm of digital forensics

Over the past two decades, improvements in the information technology landscape have made the collection, preservation, and analysis of digital evidence extremely important. This book can provide a wide-ranging overview of how AI and blockchain can be used to solve problems in digital forensics using advanced tools and applications.

Introduction -- Chapter 1. Artificial Intelligence : to strengthen or to replace traditional diplomacy -- Chapter 2. Artificial Intelligence: A New Tool For Dimplomats -- Chapter 3. Data Sovereingty: New Challenges For Diplomacy -- Chapter 4. Cyber security new threats for diplomacy -- Chapter 5. "Always There": How Diplomats Deal with Visibility Injunction in the Digital Public Sphere -- Chapter 6. Digitalization Between Security And Order And Democracy And Liberalism -- Chapter 7. Digital Diplomacy And International Regulation of The Web -- Chapter 8. Malicious Use of Artificial Intelligence, New Challenges For Diplomacy And International Psychological Security -- Chapter 9. Artificial Intelligence - The New Tool For Cyber Diplomacy, The Case of The European Union. Chapter 10. European Digital Diplomacy Towards Russia: A Cross-Country Social Media Analysis -- Chapter 11. Foreign Birds on The Wire - Goals And Rhetorical Ethos in Embassies' And Ambassadors' Twitter Communication -- Chapter 12. The Potential And Limits of US Digital Diplomacy In The Middle East And North Africa -- Chapter 13. Twitter Diplomacy on CPEC: Impact on the triangular relationships between the Pak-China and the USA -- Chapter 14. Effective Digital Diplomacy? A Case Study of The British Embassy in Egypt.

Abstract
It is widely believed that we are on the brink of another military revolution. Today, states are actively seeking to harness the power of AI for military advantage. The question of AI is therefore of profound concern to security studies scholars concerned with global issues. Up to now, the literature has tended to concentrate on AI-enabled lethal autonomous weapons; scholars have been fascinated by the possible appearance of autonomous drone swarms and their implications for security, conflict, and war. This article takes an alternative view. It argues that AI has already begun to play a significant role in military operations and is likely to be more important in the future. However, the attention to lethal autonomous weapons is exaggerated. The armed forces have principally employed AI, not to automate weapons but to help process data. AI has been used to augment military intelligence. Above all, the armed forces have harnessed AI to accelerate and improve military targeting. The article explores two recent cases where the armed forces have used data and AI to target: COVID testing in Liverpool in 2020 and the US's Security Assistance Group-Ukraine in the Ukraine War in 2022.

It is widely believed that we are on the brink of another military revolution. Today, states are actively seeking to harness the power of AI for military advantage. The question of AI is therefore of profound concern to security studies scholars concerned with global issues. Up to now, the literature has tended to concentrate on AI-enabled lethal autonomous weapons; scholars have been fascinated by the possible appearance of autonomous drone swarms and their implications for security, conflict, and war. This article takes an alternative view. It argues that AI has already begun to play a significant role in military operations and is likely to be more important in the future. However, the attention to lethal autonomous weapons is exaggerated. The armed forces have principally employed AI, not to automate weapons but to help process data. AI has been used to augment military intelligence. Above all, the armed forces have harnessed AI to accelerate and improve military targeting. The article explores two recent cases where the armed forces have used data and AI to target: COVID testing in Liverpool in 2020 and the US's Security Assistance Group-Ukraine in the Ukraine War in 2022.

Artificial intelligence (AI)-enabled digital marketing is revolutionizing the way organizations create content for campaigns, generate leads, reduce customer acquisition costs, manage customer experiences, market themselves to prospective employees, and convert their reachable consumer base via social media. Real-world examples of organizations who are using AI in digital marketing abound. For example, Red Balloon and Harley Davidson used AI to automate their digital advertising campaigns. However, we are early in the process of both the practical application of AI by firms broadly and by their marketing functions in particular. One could argue that we are even earlier in the research process of conceptualizing, theorizing, and researching the use and impact of AI. Importantly, as with most technologies of significant potential, the application of AI in marketing engenders not just practical considerations but ethical questions as well. The ability of AI to automate activities, that in the past people did, also raises the issue of whether marketing professionals will embrace AI as a means to free them from more mundane tasks to spend time on higher value activities, or will they view AI as a threat to their employment? Given the nascent nature of research on AI at this point, the full capabilities and limitations of AI in marketing are unknown. This special edition takes an important step in illuminating both what we know and what we yet need to research.

While there is growing consensus that the analytical and cognitive tools of artificial intelligence (AI) have the potential to transform government in positive ways, it is also clear that AI challenges traditional government decision-making processes and threatens the democratic values within which they are framed. These conditions argue for conservative approaches to AI that focus on cultivating and sustaining public trust. We use the extended Brunswik lens model as a framework to illustrate the distinctions between policy analysis and decision making as we have traditionally understood and practiced them and how they are evolving in the current AI context along with the challenges this poses for the use of trustworthy AI. We offer a set of recommendations for practices, processes, and governance structures in government to provide for trust in AI and suggest lines of research that support them.