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What it does

UnBias-Plus takes any piece of text, locates biased phrases at the character level, explains each one, and returns a neutral rewrite — as a structured, validated object you can drop into any pipeline.

See it on one example

Input

The senator's reckless tax scheme will devastate working families, and everyone knows the opposition always caves at the last minute.

Neutral rewrite

The senator's proposed tax plan will significantly affect working families, and commentators have noted the opposition has often changed its position late in negotiations.

high · loaded language "reckless tax scheme" — emotionally charged framing presents the policy as inherently irresponsible before any analysis.

high · loaded language "devastate" — catastrophizing verb implies certainty of severe harm.

medium · framing "everyone knows" — appeal to consensus presents an unsupported claim as common knowledge.

medium · framing "always" — universal quantifier turns a tendency into an inevitability.

Three capabilities, one output

  • Detect

    Pinpoint biased phrases at the character level. Each segment ships with start and end offsets, ready for a highlighter, an annotator, or a diff renderer.

  • Explain

    Every segment carries a bias type, a severity, and a 1–2 sentence rationale. No black-box flag — every decision is auditable.

  • Rewrite

    Get a neutral replacement per segment, plus a full rewritten version of the input. Factual content preserved; framing neutralized.

How the pipeline works

Where teams use it

  • Newsrooms and editors

    Pre-publication checks for loaded framing, sensationalism, and politically charged terminology, with the exact phrases flagged.

  • Researchers and educators

    Build datasets, study framing effects, or teach media literacy with concrete annotated examples and reasoning trails.

  • Trust and safety teams

    Triage user-generated content with structured signals: segment offsets, types, and rationales, instead of opaque scores.

  • ML and NLP teams

    A reproducible bias-analysis stage for evaluation pipelines, RAG content systems, or LLM output guardrails.