AiOfAi: Adverse Impacts and Collateral Effects of Artificial Intelligence Technologies.

Artificial Intelligence has become an integral part of modern digital ecosystems, shaping decisions across healthcare, logistics, governance, and education. By analyzing vast datasets and recognizing patterns at scale, AI has driven real societal and economic gains. Yet the same rapid integration has revealed a darker side: AI systems are increasingly exploited for deceptive, manipulative, and malicious purposes. The proliferation of deepfakes, scalable content automation, and AI-driven social engineering attacks has raised urgent concerns about trust, safety, and ethics online. These challenges are compounded by weak oversight mechanisms and the growing complexity of AI systems, making the harms harder to detect and govern.

The emerging Web era of AI presents a paradox: the same innovations that threaten security and truth also offer unprecedented solutions. In the post-truth era, generating persuasive yet deceptive content, automating social engineering, and spreading disinformation have never been easier. At the same time, AI opens exceptional opportunities for defense through intrusion detection, threat modeling, and context-aware access control. This Research Topic aims to highlight the double-edged nature of AI in the digital age. We examine how AI can be used to undermine trust, privacy, and integrity, while also serving as a foundation for more secure, ethical, and resilient digital ecosystems. We invite reflection on the societal impact of widespread AI adoption, especially Generative AI, from the erosion of public trust to the blurring of privacy boundaries, alongside the ethical and legal frameworks needed to guide responsible deployment grounded in fairness, transparency, and privacy preservation.

We welcome original contributions that present innovative ideas, proofs of concept, and use cases addressing the complex tradeoffs between AI capabilities and vulnerabilities. We aim to bring together researchers, practitioners, and enthusiasts working across AI, cybersecurity, ethics, law, and human–computer interaction to share methodologies, case studies, and tools for the AI-powered Web. Themes of interest include, but are not limited to: generative AI misuse and large language model jailbreaks; online deception, misinformation, and deepfakes; AI-enabled social engineering and fraud; privacy and data leakage; algorithmic bias and fairness; and mitigation strategies such as explainable AI, adversarial testing, intrusion detection, and governance frameworks. We encourage work on the legal and policy dimensions of responsible AI as well as technical defenses. Original research, methods, reviews, perspectives, and case studies are all welcome.

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This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
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Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: AI safety, AI misuse, Adverse impacts of AI, Large Language Models (LLMs), Jailbreaks, Misinformation, Online deception, Social engineering, Deep fakes, Privacy leakage, Data leakage

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