The InQ Dashboard is designed to demonstrate the practical application of research standards that help define information quality (InQ). It’s a dynamic tool for engaging with, evaluating, and refining the principles of InQ.

Currently, the dashboard aggregates media from diverse political and geographic sources, equipping users with clear, research-based evaluations of news content, applying AI to analyze key factors such as factual fidelity, source transparency, completeness, and sentiment neutrality. It provides descriptive insights that help users evaluate an article's reliability, bias, and quality, offering a deeper understanding of its information standards.

Using machine learning and natural language processing, the dashboard applies classifiers developed by a multidisciplinary team of experts, transforming complex information quality standards into real-time, accessible insights. AI cross-checks claims against verified databases, assesses source credibility, and detects emotionally charged language, ensuring users have the analysis they need to critically engage with the media they consume.

The dashboard plays a key role in our further exploration of InQ, with user interaction providing valuable data for ongoing research into standards and demand. By leveraging AI-driven analysis, the dashboard translates research-backed standards into practical, understandable insights that help users assess media content more effectively. As an evolving platform, it serves as a continuous experiment, applying and refining InQ standards in a transparent and adaptive manner.

Learn more about our dashboard development here.