The first is the question — and it is a question — of interpretability. As I said above, it’s not clear that interpretability is achievable. But without it, we will be turning more and more of our society over to algorithms we do not understand. If you told me you were building a next generation nuclear power plant, but there was no way to get accurate readings on whether the reactor core was going to blow up, I’d say you shouldn’t build it. Is A.I. like that power plant? I’m not sure. But that’s a question society should consider, not a question that should be decided by a few hundred technologists. At the very least, I think it’s worth insisting that A.I. companies spend a good bit more time and money discovering whether this problem is solvable.
The second is security. For all the talk of an A.I. race with China, the easiest way for China — or any country for that matter, or even any hacker collective — to catch up on A.I. is to simply steal the work being done here. Any firm building A.I. systems above a certain scale should be operating with hardened cybersecurity. It’s ridiculous to block the export of advanced semiconductors to China but to simply hope that every 26-year-old engineer at OpenAI is following appropriate security measures.
The third is evaluations and audits. This is how models will be evaluated for everything from bias to the ability to scam people to the tendency to replicate themselves across the internet.
Right now, the testing done to make sure large models are safe is voluntary, opaque and inconsistent. No best practices have been accepted across the industry, and not nearly enough work has been done to build testing regimes in which the public can have confidence. That needs to change — and fast. Airplanes rarely crash because the Federal Aviation Administration is excellent at its job. The Food and Drug Administration is arguably too rigorous in its assessments of new drugs and devices, but it is very good at keeping unsafe products off the market. The government needs to do more here than just write up some standards. It needs to make investments and build institutions to conduct the monitoring.
The fourth is liability. There’s going to be a temptation to treat A.I. systems the way we treat social media platforms and exempt the companies that build them from the harms caused by those who use them. I believe that would be a mistake. The way to make A.I. systems safe is to give the companies that design the models a good reason to make them safe. Making them bear at least some liability for what their models do would encourage a lot more caution.
The fifth is, for lack of a better term, humanness. Do we want a world filled with A. I. systems that are designed to seem human in their interactions with human beings? Because make no mistake: That is a design decision, not an emergent property of machine-learning code. A.I. systems can be tuned to return dull and caveat-filled answers, or they can be built to show off sparkling personalities and become enmeshed in the emotional lives of human beings.
Ezra Klein listed some ideas (I’ve added some bold):