Surely this comment reaches a little too far? We have to be brave enough to try new things even if they have some risk of unknown/unintended consequences.

No decision is completely safe and no system gets everything right in the first pass.

All we can ask for is a serious effort to understand risks sufficiently well to reasonably believe that the system is safe.

You can try 'new things' in the lab, until you are sure they work and are safe for general application. To field these AI solutions in lots of places where negative side effects are a serious possibility knowing that this is the case is irresponsible. Seeing this as a simple engineering problem with a simple fix-as-you-go attitude is not the right approach.

We're talking about a university research project to rescale photos here--- not a system that is fielded.

Certainly treating PULSE as a simple engineering problem and fix-as-you-go isn't so bad, is it?

Just like 'temporary software' university research projects tend to eventually get fielded in some form or other. By not tackling this problem at the root we push the onus for correct implementation down to the people that field it who thought the academics had done a proper job in the first place with all their metrics pointing to 'success'. But success isn't a success when the dataset isn't representative and datasets used by academics as a rule are not representative or have built-in biases that are very hard to overcome by engineers aiming to field these solutions.

This has already led to numerous troubles in the applications for loans, job evaluation, criminal matters and so on.

> This has already led to numerous troubles in the applications for loans, job evaluation, criminal matters and so on.

Which I'm arguing about elsewhere... but demanding that university research about upscaling a face to be perfect to publish is a little much.

> . But success isn't a success when the dataset isn't representative and datasets used by academics as a rule are not representative

Yup, so, academics should never research this stuff or publish? That certainly is an ... interesting ... recipe for progress. And research progress is one of those things that could help us ultimately address some of these issues, because as the original argument makes clear: improving the quality of datasets is not enough.