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Both in a new conference called ERAS (Engineering Reliable Autonomous Systems). The first looks at how a system - e.g., a drone on a patrol - might decide to skip some patrol points if it realises it can't reach all of them. The second attempts to catalogue the kinds of things people want from a computer explanation. Explainability is big in AI at the moment, but explanation is quite a slippery term and its not clear that the support for explainability that's being developed actually meets what people want.

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From:

a_cubed

I've seen some stuff about "explainability" that really doesn't map to anything we can turn into a logical chain of reasoning that a human would understand. It's a bit like the "fairness" issue in ML from the mid-2010s. ML researchers asked ethicists for a definition of fairness they could check for/train to and got back about 17 different definitions, mostly pretty much mutually exclusive, and certainly not averageable or anything like that.

From:

purplecat

A lot of the "XAI" stuff seems to be about visual representations of which features were important to a particular decision - so I would claim this is more an aid for expert interpretation than an explanation along the lines of Alan Winfield's "Why did you do that?".

That said, I do keep ending up in discussions with people where I try to explain that a proof is not an explanation for your average user...

From:

a_cubed

There's always an XKCD.

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purplecat

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