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Algorithms Evaluation and Challenges in Automated Hate Speech Detection for Generative AI
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Author(s): Rituraj Jain (Marwadi University, India), Ashish Sharna (Jodhpur Institute of Engineering and Technology, India), Venkateswararao Pulipati (Koneru Lakshmaiah Education Foundation, Bowrampet, India), Nausheen Khilji (JIET University, India)and Rakesh Saxena (Jai Narain Vyas University, 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.ch004
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Abstract
The research investigates automatic hate speech identification tools meant for detecting text generated by advanced generative AI systems including GPT and BERT. The text provides details about traditional machine learning as well as contemporary deep learning methodologies while putting highlight on transformer models that excel at detecting subtle and context-driven hate speech. The analysis is focused on essential evaluation metrics together with benchmark datasets but also explains implicit toxicity as well as biases through datasets and cultural interpretations challenges. This paper explores both ethical aspects and mitigation techniques alongside real-time moderation system integration practices. The section ends by providing predictive perspectives about multimodal detection methods as well as zero-shot learning and responsible AI deployment frameworks.
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