and have the results analyzed by the various stakeholders—rather like elected legislatures were originally intended to do. If we have the data that go into and out of each decision, we can easily ask, Is this a fair algorithm? Is this AI doing things that we as humans believe are ethical? This human-in-the-loop approach is called “open algorithms;” you get to see what the Als take as input and what they decide using that input. If you see those two things, you’ll know whether they’re doing the right thing or the wrong thing. It turns out that’s not hard to do. If you control the data, then you control the AI. One thing people often fail to mention is that all the worries about AI are the same as the worries about today’s government. For most parts of the government—the justice system, et cetera—there’s no reliable data about what they’re doing and in what situation. How can you know whether the courts are fair or not if you don’t know the inputs and the outputs? The same problem arises with AI systems and is addressable in the same way. We need trusted data to hold current government to account in terms of what they take in and what they put out, and AI should be no different. Next-Generation AI Current AI machine-learning algorithms are, at their core, dead simple stupid. They work, but they work by brute force, so they need hundreds of millions of samples. They work because you can approximate anything with lots of little simple pieces. That’s a key insight of current AI research—that if you use reinforcement learning for credit- assignment feedback, you can get those little pieces to approximate whatever arbitrary function you want. But using the wrong functions to make decisions means the AI’s ability to make good decisions won’t generalize. If we give the AI new, different inputs, it may make completely unreasonable decisions. Or if the situation changes, then you need to retrain it. There are amusing techniques to find the “null space” in these AI systems