Our Core Approach
- LLMs Trained on Diverse Data: We use large language models to recognize nuanced linguistic patterns far beyond simple keywords.
- Social-Science Foundations: Concepts from psychology and linguistics guide how we detect framing bias, confirmation bias, selection bias, and emotional appeals.
- Fast & Scalable Pipeline: Our infrastructure can analyze anything from a few paragraphs to large corpora in seconds, with consistent outputs.
Bias Categories We Detect
- Framing Bias: Language or tone that presents a topic as more positive or negative than a neutral account.
- Selection Bias: Facts or sources chosen in a way that excludes relevant perspectives.
- Confirmation Bias: Evidence selected mainly to support a pre-existing belief.
- Emotional Appeal: Persuasion driven by fear, sympathy, or anger rather than logic.
The links above go to Wikipedia for general reference. Our definitions may differ slightly, since we focus specifically on how these biases appear in textual content, while Wikipedia covers broader psychological and methodological contexts.
Why You Can Trust the Method
- Explainable Outputs: Reports include a bias meter, stance breakdowns, and category-level rationales to clarify why something was flagged.
- Scientifically Informed: Our criteria are inspired by experimental psychology and linguistics, adapted for robust, digital-at-scale analysis.
- Consistent & Automated: No human reviewers influence outcomes; the same input yields the same analysis, enabling reliable comparisons.
- Critical-Thinking First: AI augments judgment—it doesn't replace it. Use results as decision support alongside expert and editorial review.
- Mind Metrology Ecosystem: Check Text Bias is part of the broader Mind Metrology ecosystem, building on years of experience in metrology, social science, and data engineering.