Re:Deep Learning’s Impact on Image Processing, Mathematics, ...
by gaius on Sunday May 07, 2017 @01:02PM
(#14286327)
It didn't see him/her. Which is the extent of an explanation a human can give you. Try to picture wanting a person to explain why they didn't see a person coming from the side. We don't know what's happening inside our own heads.
There is a world of difference between "this individual driver made a human error" and "software that can't reliably see people is driving a million cars now". The developers of the software would need to demonstrate they understood why the software failed in this situation and how they are going to fix it.
I suspect we'll have similar rules for self-driving vehicles: you must wear recognisable clothing when walking through traffic
Bear in mind that we can't even make cyclists now wear hi-vis or fit lights - making the entire population wear certain things for the benefit of job-destroying technology isn't going to get many votes!
If you know the acceptable metrics and algorithms, why are you using deep learning?
I disagree, because it's a matter of scale and usefulness. An algorithm that detects pictures of cats can answer the question of "why do you think that's a cat?" with "because it looks like a cat" and that's a perfectly reasonable explanation that a human might give[0]. But the stakes are raised when you start to deal with real people in the real world. "Why did you turn down that loan application" or "why did you add that name to the no-fly list" or "why did you reject that job candidate" need a bit more exposition.
That's a self-correcting problem because no-one sane will risk their business on software that "inexplicably" just happens to reject everyone in a protected class because there happens to be some vague correlation between members of that class and some undesirable activity or outcome.
There is a world of difference between "this individual driver made a human error" and "software that can't reliably see people is driving a million cars now". The developers of the software would need to demonstrate they understood why the software failed in this situation and how they are going to fix it.
I suspect we'll have similar rules for self-driving vehicles: you must wear recognisable clothing when walking through traffic
Bear in mind that we can't even make cyclists now wear hi-vis or fit lights - making the entire population wear certain things for the benefit of job-destroying technology isn't going to get many votes!
If you know the acceptable metrics and algorithms, why are you using deep learning?
I disagree, because it's a matter of scale and usefulness. An algorithm that detects pictures of cats can answer the question of "why do you think that's a cat?" with "because it looks like a cat" and that's a perfectly reasonable explanation that a human might give[0]. But the stakes are raised when you start to deal with real people in the real world. "Why did you turn down that loan application" or "why did you add that name to the no-fly list" or "why did you reject that job candidate" need a bit more exposition.
That's a self-correcting problem because no-one sane will risk their business on software that "inexplicably" just happens to reject everyone in a protected class because there happens to be some vague correlation between members of that class and some undesirable activity or outcome.
[0] Except http://www.bbc.co.uk/news/technology-33347866 of course