New Censorship Instance

Research on censorship instances is increasingly focused on understanding and mitigating its effects across various domains, from internet access to machine learning applications. Current efforts involve developing sophisticated adversarial techniques to bypass censorship mechanisms (e.g., reinforcement learning algorithms generating obfuscated network traffic), analyzing the limitations of existing censorship methods (e.g., exploring the undecidability of semantic censorship in LLMs), and developing more robust detection methods using machine learning (e.g., latent feature representation learning for network censorship detection). This work is crucial for ensuring freedom of information online, improving the fairness and reliability of machine learning models, and understanding the broader societal impacts of censorship.