June 27, 2019

How to beat “fake news”

Most observers hold several or all of the views:

And further:

But despite all those difficulties, I also believe that a good solution to news/opinion filtering is feasible; it just can’t be as simple as everybody would like.

1. When people think about these problems, they’re probably most focused on social media platforms such as Facebook, YouTube or Twitter. But before getting to those, let’s consider the simpler case of search engines. In essence, what search engines do is:

How well does this work? I’d say that search engines:

Lessons from that start:

2. When defending against bad actors, scale helps a lot. In my favorite example:

Similarly, as the publisher of multiple blogs, I can tell you that much the same is true of WordPress’ Akismet’s fight against spam comments. Akismet isn’t perfect; indeed, I’ve stopped adding new content to the blog where this post would fit best — Text Technologies – because of a multi-year spam attack. But on the whole Akismet works very well.

Thus, in contradiction to many observers, I believe that the huge scale of social media companies is NOT the root of the problem.

3. Of course, concern is really focused on social media, and especially on the concern that people communicate things they (supposedly) shouldn’t, where:

And even if you don’t worry so much about those problems, some kind of censorship, filtering or gatekeeping is inevitable anyway, simply because there’s vastly more information in the world than any one person can consume.

So what are the main options for censorship and other gatekeeping? My opinions start:

4. So if we need gatekeeping, and no natural kind of gatekeeper can on its own be effective or safe, what’s left? In simplest terms, we need gatekeeping by (technological) committee. Mainly, what I propose comprises:

Above all, people must be able to choose their own censors.

5. What I envision for the “human-led filters to deal with various issues in credibility and bias” is something like:

Here an “organization” can be anything trusted by enough people to be economically viable, for example:

Obviously, there would be business issues, notably:

But given the importance and visibility of the problem, optimism about solving the business issues is appropriate. The hardest part is the technology itself. Can machine learning models be retrained on a sub-hour or even sub-minute basis? Sure. That’s been confirmed many times. But what I’m suggesting is a pretty complex case, with global scale, intermediate results passed among organizations, with plenty of adversarial elements, all done at very high speed.

That is not yet a solved problem. But it certainly seems solvable. Further, it’s a problem that must be solved, lest liberal democracy be as doomed as some people fear it actually is.

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Comments

2 Responses to “How to beat “fake news””

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