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> If you can't explain the model, it means you don't know the assumptions that went into the model's output This is true, but there are many, many kinds of mod
by fjp 8y ago
> If you can't explain the model, it means you don't know the assumptions that went into the model's output
This is true, but there are many, many kinds of models that have basically zero explanatory power but have higher predictive capabilities than models that are easier to explain. They have been around a long time and are used for many different practical applications.
Unfortunately, the draw of that seemingly infallible super-high-predictive capability will almost certainly be heavily involved in financial markets before long. I have no problem if some people want to risk a bunch of money in a hedge fund that uses neural net models or whatever else, but having enough money controlled by these models could pose a serious systemic risk.
- wpietri 8y ago> having enough money controlled by these models could pose a serious systemic risk This is the part that worries me. For a decade before the 2008 financial collapse, people were quietly saying, "Gosh, there's a lot of activity in derivatives and we don't really know where the risk is going." One of many factors there was the way rating agencies gave very generous ratings to mortgage securities. Critics note that it was in their short-term financial interest to do that. If people can screw up that badly with models they supposedly understand, it seems to me to be even more risky when working with models where people have just given up understanding and put their faith in the AI oracle. As long as they get the answers that maximize their end-of-year bonus checks, they have a strong incentive not to dig deeper.
- zeroname 8y agoIn my estimation, existing models are just oracles with less variables. > One of many factors there was the way rating agencies gave very generous ratings to mortgage securities. Garbage in, garbage out.