That doesn't sound exactly legal as the actual reason had to exist at the time of the decision and an after the fact reason would not be the truth.
Don't the ML models log why they made decisions in the first place?
If the outcome of algorithm/model can't be explained by its regulators then it shouldn't be making any decisions of any significance or importance to human lives.
When engineers fail to address cracking in a bridge and people die when it falls, humans are held to account and explain why this decision was so. If machine learning statistical models can't do the same, they better not be used to scan any bridges for structural cracking where I live and vote.
I don't have to know the complexities of how all the factors are interacting within a neural net to be able to tell someone that if they increased their credit score by 100 points, they'd be approved.
That's almost certainly never going to happen. Our most accurate models are unintelligible, and our most intelligible models are inaccurate. There's a trade-off here and without some sort of magic I don't see us transcending that trade-off.
Also accuracy is hard to talk about if you don't have the whole distribution you are estimating from; which generally we don't... Many highly accurate models (in the sense of working on the test set and n-fold xvalidation) underperform in production.
Going further, how in the heck do they justify using something like this on credit applications or anything else that can adversely affect their fellow humans? The ethics of such a lack of knowledge should give pause and frankly I hope they get sued if this is the truth of the matter. That is monstrous.
They understand that by evaluating the model on a "test dataset" that is, hopefully, representative of the real-world. This does allow you can explain why a given model makes the decisions it makes - it only allows you to understand how well it performs on the data you feed it.
Using an inaccurate or biased model to detect assign credit ratings is indeed immoral. This is orthogonal to interpretability: you can have a model that is both inaccurate and interpretable (i.e. it's wrong and you can say why it's wrong); you can also have a model that is highly accurate but not interpretable (i.e. it works, but you cannot explain why).
Sometimes it's possible to extract a sort of conceptual understanding in some cases, eg you might say that this layer performs edge detection or whatever. But that's not much of an explanation.
They do it because it works. You trust your Uber driver to get you to your destination even though you have no idea how his neurons do it. People are going to trust machine intelligence in the same way.
Given the xenophobic history of the human race, I have some severe doubts about that. Frankly, the first data scientist who ends up in court because the automated car decided to kill some kid on the sidewalk[1] to keep the death count down is going to find out real quick what a jury thinks of machine intelligence.
Thinking about it, I can see the black box in a car having to record the last minute of instructions run by the car's cpu. The whole idea of unknowable isn't exactly going to sit well with the NTSB. I can truly see this if data scientists testify in a Congressional Hearing that they don't understand how their creation came to its decision.
I guess I am in awe of folks who do not have tools to figure out what model all the learning has built. Where is the DTrace for ML? If your neural network has 50 million parameters then how the heck did the data scientist have a data set to teach it from that is in anyway complete enough to trust it?
I can just not see how it ethically can be unleashed upon people in a final go / no go decision affecting people's lives or livelihood. After reading this thread, I now hope people who are rejected for loans ask for an actual explanation and the exact manner the decision for their rejection was reached.
1) https://www.usatoday.com/story/money/cars/2017/11/23/self-dr...