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Techniques for Detecting Hate Speech in AI and Human-Generated Content
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Author(s): Chandrashekhar Goswami (Faculty of Computing and Informatics, Sir Padampat Singhania University, Udaipur, India), Ansar Sheikh (St. Vincent Pallotti College of Engineering and Technology, Nagpur, India), Anand Bhaskar (Sir Padampat Singhania University, Udaipur, India)and Dipesh Vaya (Sir Padampat Singhania University, Udaipur, India)
Copyright: 2026
Pages: 30
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.ch013
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Abstract
Hate speech detection is critical due to the rising risk of online and offline offensive material. This chapter reviews methods for detecting hate speech in human-generated and AI-generated content, addressing differences, obstacles, and potential solutions. Various methods detect explicit and implicit hate speech, including rule-based systems, traditional machine learning models, and deep learning. Key issues include biases in model outputs and challenges in recognising AI-generated hate messages, with discussions focusing on fine-tuning pretrained models and multimodal approaches to improve AI-generated content detection. The chapter also examines ethical implications, such as balancing free speech and censorship, privacy issues, accountability in AI algorithms, and fair decision-making. It identifies future trends, including natural language processing (NLP) advancements, real-time detection systems, and evolving regulatory frameworks for AI content moderation.
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