Mission & Values

Deepnews.ai is based on a simple idea: building a system to spotlight quality journalism from the web, in real-time and at scale.

  • Quality journalism is essential for preserving and fostering democracy. Right now, it is diluted in a torrent of noise that has little to do with being informed. 
  • Misinformation is a real threat to democracy. Today, there is not a single election unaffected by toxic news. No Artificial Intelligence system can be used efficiently to fight misinformation. 
  • However,  A.I. and natural language processing can vastly help quality journalism to emerge from the background noise. Therefore making original reporting more visible and increase its economic value. 

That’s why we developed Deepnews.ai.

We regard A.I. as a powerful tool but not as a solution to everything

The Deepnews Scoring Model, which is at the core of our system, carries all the promises and the uncertainties of the field. It’s a black box, and so we don’t know exactly how the model scores the articles we submit (although we have some clues). However it is highly scalable, and our algorithm is tamper-proof as it would require considerable resources to be reverse-engineered.

We are a tech AND editorial company

Deepnews.ai is the result of a unique alignment between two spheres that historically barely speak to each other: journalism and engineering. We have spent hundreds of hours learning to understand each other’s businesses, such as what makes a good story (a challenging question), or how to translate components of great journalism into a mathematical framework.

This is just the beginning

Right now, Deepnews.ai and its proprietary scoring technology are a work in progress. A year from now, they might have morphed into something completely different. In the A.I. field, technology evolves at lightning speed, as new tools show up every quarter. We also know about the intrinsic imperfection of any machine learning model is the data that feed them. We take great care to develop the least possible biased information model by constantly refining it.