About BiasChecker.ai

Our Mission

BiasChecker.ai is dedicated to promoting media literacy and empowering critical thinking in the digital age. We help individuals and organizations identify and understand bias, manipulation, and other issues in the content they consume and produce.

In an era of information overload, where content reaches us faster than ever before, the ability to recognize bias is essential for making informed decisions and maintaining a balanced perspective.

For the full story of what BiasChecker.ai does and the principles behind it, read our introduction to BiasChecker.ai.

Our Team

BiasChecker.ai is built by a small, bootstrapped team driven by a passion for transparency and informed discourse.

Our team combines technical excellence with a commitment to building tools that make a meaningful difference in how people engage with information.

Our Technology

BiasChecker.ai leverages state-of-the-art artificial intelligence, powered by advanced Large Language Models (LLMs), to analyze content for potential bias, manipulation, and other issues. Our analysis pipeline — whose methodology and prompts we publish openly — is designed to identify and evaluate:

• Cognitive biases (e.g., confirmation bias, availability bias)
• Social biases (e.g., gender bias, racial bias, age bias)
• Source biases (e.g., authority bias, funding bias)
• Presentation biases (e.g., framing bias, cherry-picking)
• Linguistic biases (e.g., loaded language, presupposition)
• Logical fallacies (e.g., straw man arguments, false dichotomies)
• Manipulation techniques and emotional appeals
• Legal, scientific, moral, and historical accuracy concerns

Why we use large language models

Large Language Models are well-suited to analysing media content:

• Comprehensive Knowledge: Trained on vast amounts of diverse text, enabling recognition of subtle linguistic patterns and nuances that may indicate bias.
• Contextual Understanding: Unlike rule-based systems, LLMs understand context and nuance, which is crucial for identifying bias that depends on broader context.
• Multi-format Adaptability: Our system analyzes content across various formats, from news articles to social media posts and research papers.
• Holistic Analysis: Simultaneous analysis of tone, word choice, argument structure, and source credibility provides comprehensive assessments.
• Transparent Reasoning: Our system not only identifies issues but articulates the reasoning behind its assessments, providing valuable insights.

What We Deliver

After analysis, BiasChecker.ai provides detailed reports including:

• Types of bias and issues detected
• Specific examples from the analyzed content
• Assessment of potential impact
• Context and explanation for each finding

No model is free of blind spots — which is why BiasChecker lets you run the same article through several AIs side by side, or ask a panel of models from rival labs for a consensus verdict. How we analyse, and the limits of it, are documented on our Methodology page.

For a comprehensive list of the bias types we detect, visit our Bias Types page.

How You Can Use It

• In your browser: our Chrome extension analyses the article you are reading and shows results in a side panel — thirteen analysis lenses, from bias and persuasion to a synthesised analyst's report. (Join the waitlist — launching soon.)
• On this site: paste any text and run the same analyses, no extension needed.
• Across AI models: compare how different models judge the same article, or run a multi-model consensus.

Why Understanding Bias Matters

In today's information landscape, recognizing bias is more important than ever. Biased content can:

• Distort understanding of critical issues
• Reinforce information bubbles and echo chambers
• Influence public opinion and decision-making
• Create divisions and misunderstandings between groups

By illuminating these issues, we aim to provide a clearer, more balanced view of the information landscape and foster more informed discourse.

Our Principles

Transparency: We are open about our methodology and acknowledge that no system is perfect. We encourage users to think critically and use our tool as one of many resources.

Objectivity: BiasChecker.ai does not take editorial positions or tell users what to think. We provide analysis and insights to help users form their own informed opinions.

Privacy: We respect user privacy. We never sell your data. Community results for public web pages are stored by article content, not by who analysed them; text you paste for analysis is stored privately in your own account. See our Privacy Policy for the details.