5 ms·

I don't see why the gaming problem you describe matters in anything but the absolute shortest term. If algorithms are gamed they'll have to be modified to be r

I don't see why the gaming problem you describe matters in anything but the absolute shortest term. If algorithms are gamed they'll have to be modified to be resistant to gaming in order to deliver effective results. As the game of cat and mouse goes on it would result in us refining our algorithms to the point where they actually measure what we want rather than an approximation of what we want. Eventually "gaming the algorithms" would be the same as producing high quality content. Yes refining algorithms to that point would be hard work but it would get figured out.

jasode 7y ago
>If algorithms are gamed they'll have to be modified to be resistant to gaming As far as I know, nobody in computer science has come up with a ranking algorithm that can't be gamed to render the algorithm useless. (Unless the so-called algorithm is a trivial case such as the runner with the fastest time across the finish line is the "winner" of the race.) For a multi-criteria algorithm that approximates a hazy idea like "video quality" that uses various signals as proxies for quality, the algorithm's exact source code must be kept a secret. Yes, you can publicize broad outlines of what the algorithm does but you can't reveal the actual code (or the actual machine learning weights, etc). Yes, an algorithm like a SHA256 hash source code can be made transparently public and still can't be "abused" such that a bad actor can make the string "DEADBEEF" appear anywhere they want in the output hash. This class of algorithm behaves differently than ranking/recommendation algorithms.