Why scan AI output for bias
Language models absorb the patterns of their training data, including loaded framing, stereotypes, and political slant. When that text is published — in support replies, marketing copy, news summaries, or hiring materials — those signals carry real consequences. This tool gives you a fast, transparent first pass: it highlights specific phrases that commonly indicate bias so a human can decide whether each one belongs.
It deliberately does not use another AI model to judge bias, which would only add a second model’s blind spots. Every flag is a transparent, explainable keyword match you can audit.
How it works
The scanner checks your text against a curated list of bias markers grouped into five categories:
- Loaded language — words like obviously, clearly, so-called, and regime that smuggle a judgement past the reader.
- Absolutist — always, never, all of them, every single, which overgeneralise across groups or cases.
- Stereotype framing — those people, by nature, naturally better, which essentialise or other a group.
- Gendered assumptions — mankind, chairman, female engineer, which mark gender unnecessarily or default to one.
- Political slant — charged buzzwords and pejoratives from across the spectrum.
Each match shows the phrase, its category, and a one-line explanation, with a per-category tally at the top. You can filter to a single category to focus a review.
How to use the results
Treat every flag as a question, not a verdict. “Clearly” is fine in a maths proof and a red flag in a news summary; “regime” is loaded in most contexts but neutral in “exercise regime.” The value is that the scan forces you to look at the phrases that most often hide bias, instead of skimming past fluent text. Pair it with a human reviewer for anything published, and remember that the absence of markers does not mean the absence of bias — subtle framing and selective omission leave no keyword trail.