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>> their models have 20,000 vectors in determining credit worthiness. How would you begin to break that down to something explainable? Well, somehow they decid
by kaijush 10y ago
>> their models have 20,000 vectors in determining credit worthiness. How would you begin to break that down to something explainable?
Well, somehow they decided that their 20k-parameter model is accurate. They should at least be able to explain why they took that decision, even if the model itself is too complex.
- JoeAltmaier 10y agoBecause the training set was covered with x% accuracy. Ok, how does knowing that help?
- kaijush 10y agoOK, so we're assuming supervised learning. In that case, how were the features of the training set chosen? And how was the training set labelled? Most of the time, outside of semi-supervised learning, those things are not in the data, someone has to (painstakingly) decide them. That can very well be explained I believe. More generally, what I mean to say is that you can learn any number of models from the same data, so to a great extent you choose what your model learns by manipulating your algorithm's (hyper)parameters, the training set's features, choosing what goes into the training set itself etc etc. That process leaves room for a lot of regulatory oversight. - in principle. In practice I expect since we're talking about people lending money or selling insurance we're only ever going to get those details out of their cold dead hands, and only with considerable effort at that.
- dharma1 10y agoThey validate the accuracy of the model with real users, starting with short term/low value loans, to mitigate risk. The explanation for them is - it works.
- tomaskafka 10y ago"How would you find out that it doesn't work? For a specific group of people for example?"
- dharma1 10y agoHow do you find out that it doesn't work? Some loans default, company loses money on those. The model gets updated continuously from the real user data, so it learns from the mistakes