Detecting Hate Speech in Human and AI-Generated Content

As digital communication becomes increasingly pervasive, the detection of hate speech in both human and AI-generated content has emerged as a critical concern for online safety. The use of harmful language across social media platforms and content generated by language models poses significant challenges in identifying toxic discourse. Traditional moderation methods often fall short in recognizing nuances prompting the integration of machine learning and natural language processing techniques to enhance detection accuracy. This evolving field underscores the need for robust systems capable of distinguishing between free expression and harmful language in this era. Detecting Hate Speech in Human and AI-Generated Content: Techniques, Bias Mitigation, and Ethical Considerations addresses the pressing challenge of hate speech detection across both AI-generated and human-generated content. It fills a crucial gap, providing a dual-focused approach to detect and manage hate speech effectively in this new, mixed-content landscape. Covering topics such as deepfakes, moderation, and social media, this book is an excellent resource for researchers, academicians, students, policymakers, and more.