Imagine an AI that not only flags harmful content but also explains why it's problematic, like a helpful editor for your AI's writing. That's the idea behind SAFETY-J, a new bilingual (English and Chinese) AI system designed to evaluate the safety of text generated by other AIs. Why is this important? Current AI safety tools often work like a black box, simply labeling content as "safe" or "unsafe" without explaining their reasoning. This makes it hard to understand their decisions and improve the AI's behavior. SAFETY-J tackles this by providing detailed critiques alongside its safety judgments. It's like having a dedicated safety consultant that points out potential problems and suggests improvements. Researchers built SAFETY-J using a massive dataset of text, including dialogues and query-response pairs, covering a wide range of safety issues, from hate speech and misinformation to privacy violations. To ensure its critiques are helpful, they created an automated system to evaluate critique quality, enabling continuous improvement. The results are impressive. SAFETY-J not only outperforms other safety evaluation models but also helps improve the safety of AI-generated text in real-world scenarios. It can even be customized with specific rules, offering a more tailored approach to content moderation. This research opens exciting new possibilities for making AI safer and more transparent. By explaining its reasoning, SAFETY-J empowers developers to understand AI safety better and fine-tune models to generate responsible and ethical content. It's a step toward building more trustworthy and accountable AI systems.