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Highlighting the Challenges of Bias and Fairness in Hate Speech Detection
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Author(s): Manish Mittal (Brainware University, India), Manish Tiwari (Sir Padampat Singhania University, India), Ruchi Doshi (ResAIShala Technocrats Pvt. Ltd., Udaipur, India)and Kamal Kant Hiran (Sir Padampat Singhania University, 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.ch007
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Abstract
This chapter offers a comprehensive overview of the challenges, advancements, and ethical concerns in automated hate speech detection. It critiques early keyword-based methods and highlights the shift toward advanced, context-aware, and culturally informed AI models. Key issues include bias in training data, subjectivity in annotation, and evolving adversarial tactics. The chapter emphasizes the essential role of human oversight and advocates hybrid human-AI moderation for balanced judgment. It stresses the importance of transparency, accountability, and fairness within complex legal and regulatory frameworks. Addressing technological limits such as multimodal data and annotation quality, it calls for interdisciplinary collaboration and ongoing innovation. Ultimately, it argues that effective moderation must protect free expression and vulnerable groups, uniting technical strength with ethical responsibility.
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