Lion Cipher Lab
Detect toxic content with a model
- AI Security
- Beginner
- ~40 min
- Free · no account required
Why it matters
Every large platform that hosts human conversation faces the same problem: far more content arrives every minute than any team of people could ever read. Trust and safety teams lean on automated classifiers to triage that flood, so a small model quietly shapes which comments get seen, hidden, or sent to a human reviewer. That makes toxicity detection one of the highest-stakes places AI touches ordinary people, and one of the easiest to get wrong. A false positive is not a rounding error; it is a real person silenced for something they did not do, and these models are known to fire more often…
What you'll learn
- content moderation basics
- running a toxicity classifier offline
- reading a probability score
- setting and reasoning about a threshold
- recognizing false positives
- understanding model bias against dialects and identity terms
- keeping a human in the loop
Steps in this lab
- What a toxicity classifier actually is
- Why platforms use one: moderation at scale
- Install a local toxicity model, offline-first
- Score some sample text and read the probability
- Set a threshold, and meet the false positive
- The bias problem: dialects and identity terms
- Context, reclaimed language, and the human in the loop
- What you can now do, and hold onto