AI might be best put to use for finding loopholes. In formal specifications, in legal documents, in laws... you name it.

The required process change is reasonably straightforward; instead of presuming an answer given by AI is useful as a solution to the given problem, take it as an indicator of a loophole in problem specification and see if one can be closed up.

Courts are of course have a certain degree of interpretive freedom, but the process should result in a machinery that implements intent faithfully, and legislators change laws to change intent (and to patch holes, but in common law that usually happens through the courts too).

Legislation, laws, and law enforcement as technologies have always had these trade offs, and they have progressed tremendously over the centuries (or millennias).

From the Roman veto of the tribune of the plebs, to current constitutional review there are many checks and balances. But legislation as a process doesn't have inherent loopholes.

Currently for every piece of law proposed many official and civil groups warn of the potential unintended side effects of it. But then the majority usually disregards those concerns.

This. The use of "gaming" to describe the observed behaviour seems strange. To "game" suggests some malfeasance in the algorithm; a deliberate effort to subvert the intended goal, which in turn implies knowing what the "real" goal was - and somehow deciding to shortcut it.

That isn't the case. Rather, the problem lies in appropriately specifying the desired outcome, and any relevant constraints that must be obeyed. Indeed, DeepMind has separately written about the "specification problem" elsewhere [0].

That's in no way to downplay the difficulty in specification. It's one we've wrestled with in software for decades.

> Either you have the mathematical and stochastic skills to describe your problem criteria as a differentiable function, or not.

Indeed. That statement could equally have been written ~30 years ago when Z, VDM and the likes were active areas of research. More recently, TLA+ has gotten some traction, but it's still very much in the margins.

Perhaps ML will spur the next wave of specification formalisms. But it will face the same challenge as its pre-decessors: how to make it accessible to more than a very (very) small set of sufficiently capable few.

[0] https://medium.com/@deepmindsafetyresearch/building-safe-art...

We could be just around the corner, but the fact that people are taking for granted that there's no intelligence in AI isn't promising.

If you respond defensively about how difficult it is to give people what they want, well, that's the whole point.

For example, people were willing to declare optical flow "solved" already 5 years ago. Yet, autonomous drones still crash into wires, glass, trees, snow, water, etc., because their optical flow algorithms cannot handle these aspects of reality (which were not present in the AI training data).