[olympus]

Expand that a bit:

"Articles 13-15 provide rights to 'meaningful information about the logic involved' in automated decisions."

Your scenario doesn't explain the logic. Saying "that's the AI's choice and we're going with it because it's 99.9% accurate" isn't the logic involved in the decision.

You need an interpretable model to ensure that the AI isn't discriminating based on a protected class (race/gender/etc). "You were denied a loan because the AI determined that you're Polish, and we don't like Polish people" is partly what this law wants to prevent.

Forcing models to be explainable makes sure that we aren't illegally discriminating, so we need to make sure that we can tell why the AI made it's choice, not just what the choice was.

[wiz21c]

100% agree, that's why I wrote "that conclusion based on these data : X,Y,Z". The important word is "data". I said data because with AI, the decision process may be quite a black box. The only thing you know is what data you put in. So to me, input data is part of the answer.

In my job, we grant decisions to help people or not. We could use some kind of AI to give, for example, a "pre decision". That AI would be trained on our current data but, in the end, it would interpret the profile of the person. So basically, it'd say "based on the profile of X, we've decided that ...". Now if nationality, for example, was in the list of data in the profile, I'm 100% sure that we'd have a lawyer at our door (rightfully).

[olympus]

My point is that just saying "we have data X,Y,Z" for a person doesn't explain the logic. It allows you to check that the input data is correct, but you don't understand the decision from it. What you need is an explanation saying something like "X is too low, and we think that Y in the presence of Z is a significant risk factor."

The need for the explanation is because an AI can learn to discriminate against protected classes even if they aren't explicitly part of the dataset. You might not have included race in the inputs, but you did include their name, and it figures out that people named "Jakub" should be declined for a loan. The AI can't say that it's because they are Polish, but it learned to discriminate against Polish sounding names because of all the racism in the training data. We could uncover that if the AI was able to explain that it denied the loan mostly because of the name, and that the other pieces Y and Z did not factor into the decision as heavily. Just saying X Y and Z doesn't help us figure out which of those pieces are the important parts for denying a loan.