What is this?

This is a news aggregator site developed by a group of researchers at MIT and elsewhere to give you control of your news consumption, as explained in this video.

Just as it's healthier to choose what you eat deliberately than impulsively, it's more empowering to chose your news diet deliberately on this site than to randomly read what marketers and machine-learning algorithms elsewhere predict that you'll impulsively click on. You can make these deliberate choices about topic, bias, style, etc. by adjusting sliders described here.

How is this funded?

We have an ongoing research project led by Prof. Max Tegmark on how machine learning can be used to classify news. Since this news aggregator is fully automated, running it as an ad-free public service costs us nothing except our cloud computing bill, which at our current (April 2020) traffic levels comes to less than $10/month.

How does it work?

We're planning to open-source our machine-learning algorithms on GitHub once they're accepted for publication.

Won't this contribute to filter bubbles?

There's a rich scientific literature on how click-optimizing algorithms at Facebook, Google, etc. have polarized and divided society into groups that each get exposed only to ideas they already agree with. So won't giving people choices such as the left-right slider on this site exacerbate the problem? Recent work from David Rand's MIT group suggests the opposite: that people become less susceptible to fake news and bias when given easy access to a range of information, enabling what Kahneman calls "system 2" deliberation instead of "system 1" impulsive clicking and reacting. Their work also suggests that many people are interested in opinions disagreeing with their own, if expressed in a nuanced and respectful way, but are rarely exposed to this. So perhaps we should not rush to blame consumers rather than providers of news.

What is your privacy policy?

You'll find our privacy policy here.

How can I contact you with feedback?

This is work in progress, and as you can easily tell, there's lots of room for improvement! Please help us make it better by providing your feedback here.