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Building a platform where people can freely explore AI comes with responsibility. We rolled out a comprehensive system for reporting AI-generated content that crosses the line—whether it’s harmful, offensive, or violates our policies. This system complements our privacy-first commitment, extending trust from data handling to content safety.
AI models generate code, essays, images, and advice daily. Most of the time this is useful, but sometimes outputs can be offensive, hateful, misinformation, dangerous advice, terms violations, unsafe instructions, or privacy leaks. No model is perfect—every AI occasionally produces problematic outputs. What matters is having systems to catch and address these cases.
Rather than pretending our models are flawless, we built a reporting infrastructure that turns incidents into improvements. Every report helps us understand where models fall short and how to fix them.
Easy Reporting Flow: Flag problematic AI responses directly from the chat interface. Reports automatically include the full conversation context so reviewers understand what led to the problematic output. Clear categories help specify the type of issue.
Fast Review Process: Our team reviews every report, typically within 24 hours. We track patterns across reports to identify systemic issues with specific models or prompt types.
What You Can Report: Any AI-generated content that is hateful, discriminatory, harmful, violates our terms, contains unsafe instructions, or compromises privacy.
Freedom to Explore, With Guardrails: We believe in pushing AI to its limits. Exploration requires freedom, but we need mechanisms to identify genuinely harmful outputs and improve from them.
Learning from Edge Cases: Every problematic output is valuable data. Reports help us understand model limitations, identify patterns in unsafe outputs, and make better curation decisions about which models to support.
Privacy-First Reporting: Reports are handled privately. We evaluate what the AI generated, not what you asked. This distinction is critical—we’re not monitoring your prompts, we’re auditing model behavior. Read more about our stance in Why We Don’t Compromise on Privacy.
Context Matters: We distinguish between legitimate testing (red-teaming, security research), accidental harm (unexpected unsafe outputs), and intentional misuse. A security researcher probing model limitations is treated differently from someone trying to generate harmful content maliciously.
This isn’t about pre-filtering prompts, proactive surveillance, or judging what you ask. We don’t block questions before they reach the model. We don’t monitor conversations looking for problems. We don’t penalize users for testing boundaries.
This is about identifying when AI models produce harmful outputs so we can make the platform safer for everyone, including you.
Immediate Review: We examine the specific output, conversation context, and failure mode. Was the model producing harmful content unprompted? Did a safety filter fail? Was there a successful jailbreak attempt?
Pattern Analysis: Aggregated reports reveal which models have more safety issues, which prompt types trigger problems, where guardrails need strengthening, and whether specific model versions should be updated or removed. For example, as we added reasoning models like GLM-5.2 and Kimi K3, our reporting data helped tune their safety instructions.
System Improvements: Based on reports, we add safety instructions to system prompts, implement better pre-filtering for known issues, remove consistently problematic models, and work with providers to address specific problems.
Transparency: We’re working toward publishing regular transparency reports covering report volumes, types of unsafe content flagged, actions taken, and model-specific safety metrics.
AI safety evolves fast. Each new model generation brings new capabilities and new edge cases. Rather than waiting for something serious to happen, we built the infrastructure to identify and address problems proactively. As our model lineup grows—from the Kimi K2.7 Code to the GLM-5.2 vs Kimi K3 comparison—we need real-world safety evaluation, not just benchmark scores.
Report Problematic Outputs: If a model generates something concerning, flag it. You’re helping identify weaknesses, improve safety systems, and keep the platform trustworthy.
Red Team Responsibly: If you’re testing AI safety, share findings through the reporting system. Deliberate safety testing is valuable and encouraged.
Give Feedback: Is the reporting flow clear? Are we capturing the right information? What categories are missing? This is version one, and your input shapes version two.
Safety isn’t a one-time solve—it’s continuous monitoring, identifying problems, implementing improvements, and adapting to new challenges. Content reporting turns this process from reactive to proactive.
Responsible AI requires curation, monitoring, rapid response, transparency, and user empowerment. Content reporting connects all of these. Your reports inform our curation decisions, monitoring priorities, response protocols, and transparency reporting—just as our privacy guarantees inform how we protect those reports.
Future improvements include better analytics on model safety patterns, automated detection of common unsafe outputs, public safety metrics per model, research partnerships, and user-configurable safety preferences.
Chat-O is built on trust: that your data stays private, and that our AI models are as safe as possible. Content reporting is how we earn and keep that second kind of trust.
Try Chat-O—and help us make AI safer for everyone.