The future of democracy depends on having informed voters. Therefore, the electorate needs to be aware of the validity of the information they receive in the media. Through design research, we developed a speculative online aggregate news platform that uses a decentralised algorithm to assess the validity of digital content.

Like social media, anyone can submit content to be published on the platform. However, each publisher has an associated 'credibility score', that evolves and is affected by the authentication results of their previous submissions. This score influences the number of users who consume the publisher's content.
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Authentication is executed by randomly selecting a parked car and running an open-source algorithm on its internal computer. Cars operated as an expensive computer protects the system from Sybil attacks, while encryption ensures biases do not influence the authentication. The algorithm identifies information that can be proven true or false and opinions that cannot be proven either way. These classifications slowly fade over time to account for new contradictory information that may emerge and show that the authentication was most valid at the time of publishing.

When users interact with content about a topic that is gaining significant attention, they are presented with an Opinion Map. It combines all content about the associated issue, assessing them using Natural Language Processing and visually grouping them by their opinion. The user's autonomy to explore information in this map functions as a softer way to break echo chambers than binary fact-checking.

Read the research report

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