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Social sampling, very simply, is looking around you at the actions of people who are like you, finding what’s popular, and then copying it if it seems like a good idea to you. Idea propagation has this popularity function driving it, but individual adoption also is about figuring out how the idea works for the individual—a reflective attitude. When you combine social sampling and personal judgment, you get superior decision making. That’s amazing, because now we have a mathematical recipe for doing with humans what all those AI techniques are doing with dumb computer neurons. We have a way of putting people together to make better decisions, given more and more experience.

So, what happens in the real world? Why don’t we do this all the time? Well, people are good at it, but there are ways it can run amok. One of these is through advertising, propaganda, or “fake news.” There are many ways to get people to think something is popular when it’s not, and this destroys the usefulness of social sampling. The way you can make groups of people smarter, the way you can make human AI, will work only if you can get feedback to them that’s truthful. It must be grounded on whether each person’s actions worked for them or not.

That’s the key to AI mechanisms, too. What they do is analyze whether they performed correctly. If so, plus one; if not, minus one. We need that truthful feedback to make this human mechanism work well, and we need good ways of knowing about what other people are doing so that we can correctly assess popularity and the likelihood of this being a good choice.

The next step is to build this credit-assignment function, this feedback function, for people, so that we can make a good human-artificial ecosystem—a smart organization and a smart culture. In a way, we need to duplicate some of the early insights that resulted in, for instance, the U.S. census—trying to find basic facts that everybody can agree on and understand so that the transmission of knowledge and culture can happen in a way that’s truthful and social sampling can function efficiently.

We can address the problem of building an accurate credit-assignment function in many different settings. In companies, for instance, it can be done with digital ID badges that reveal who’s connected to whom, so that we can assess the pattern of connections in relation to the company’s results on a daily or weekly basis. The credit-assignment function asks whether those connections helped solve problems, or helped invent new solutions, and reinforces the helpful connections. When you can get that feedback quantitatively—which is difficult, because most things aren’t measured quantitatively—both the productivity and the innovation rate within the organization can be significantly improved. This is, for instance, the basis of Toyota’s “continuous improvement” method.

A next step is to try to do the same thing but at scale, something I refer to as building a trust network for data. It can be thought of as a distributed system like the Internet, but with the ability to quantitatively measure and communicate the qualities of human society, in the same way that the U.S. census does a pretty good job of telling us about population and life expectancy. We are already deploying prototype examples of trust networks at scale in several countries, based on the data and measurement standards laid out in the U.N. Sustainable Development Goals.

On the horizon is a vision of how we can make humanity more intelligent by building a human AI. It’s a vision composed of two threads. One is data that we can all trust—data that have been vetted by a broad community, data where the algorithms are known and monitored, much like the census data we all automatically rely on as at least

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