> Ignorance of your "black box" excuses not
Hopefully the FTC understands linear separability. Because I often get the impression that people who want ML models to be explicable don’t, and are expecting the mathematically impossible.
Hopefully the FTC understands linear separability. Because I often get the impression that people who want ML models to be explicable don’t, and are expecting the mathematically impossible.
Or at least mean that if you're too poor for a doctor or lawyer you can't fall back to the unreliable AI?
Quantifying your risk does not mean you eliminated all your risk.
We allow the sale of alcohol, tobacco, and firearms too.
Perhaps you can’t perfectly explain it but you can at least understand it statistically.
For example is not currently possible to mathematically explain people’s behaviour, but there is statistical evidence and also accountability on an individual. Eg a doctor making a decision about a scan result.
The following would seem to be OK: "This model performs well in a set of tests we devised, your mileage may vary."
If you can't explain the ML part due to whatever math detail, you better make sure you write a good wrapper around it to catch bad output which you can explain.
It’s cobbling together an explanation from texts that explain how to solve a problem. It’s like a student who copies an answer, then copies the explanation as well.
> Experiments on three large language models show that chain of thought prompting improves performance on a range of arithmetic, commonsense, and symbolic reasoning tasks. The empirical gains can be striking.
The result was generated via a deep neural network, not by whatever explanation or reasoning it prints out when asked.