when an algorithm becomes very popular (makes more decisions), people have more reasons to game it.
To err is algorithm: Algorithmic fallibility and economic organisation

The consequences of this “gaming,” especially if it is done by algorithms rather than humans, needs to be given a closer attention. Consider the following (from this post.)

if I churn out random false models, a certain portion of them will pass as false positives. If I publish only those models that passed the threshold, in the extreme case all of them may be false.

In this example, the algorithm is not “gaming” the process, per se — just sampling in highly biased fashion. But, in the end, the consequence is the same.

One clap, two clap, three clap, forty?

By clapping more or less, you can signal to us which stories really stand out.