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AI-Generated Hate Speech Detection
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Author(s): Neha Yadav (G.L. Bajaj Institute of Technology and Management, Greater Noida, India), Mayank Singh (SS University, Noida, India)and Vipin Tyagi (Jaypee University of Engineering and Technology, Guna, India)
Copyright: 2026
Pages: 24
Source title:
Detecting Hate Speech in Human and AI-Generated Content: Techniques, Bias Mitigation, and Ethical Considerations
Source Author(s)/Editor(s): Mohammad Arsalan (Qatar University, Qatar), Mehul Mahrishi (Swami Keshvanand Institute of Technology, India), Ruchi Doshi (Universidad Azteca, Chalco, Mexico), Archika Jain (Swami Keshvanand Institute of Technology, India)and Chandrashekhar Goswami (Sir Padampat Singhania University, India)
DOI: 10.4018/979-8-3373-3063-1.ch006
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Abstract
The advent of large language models (LLMs) in generative artificial intelligence (AI) has fundamentally changed the scope of hate speech online. Where hate speech was traditionally unambiguously, human using AI can now produce aggressive, degrading, or even coded language with incredible fluency, speed, and variation. This chapter investigates the emergent technical, social, and ethical issues of hate speech produced by AI. In addition, the chapter includes a broad survey of the history of hate speech in the internet era, highlights the subtle distinctions between offensive language and hate speech, and provides an analysis of the problematic nature of AI-generated threats, including linguistic variability, scale, and semantic ambiguity. The chapter concludes with thoughts on what it will take to protect digital public spaces in the age of generative AI, including continued technical advances, collective social advancements, and ethical collaborative work across disciplines.
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