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> Moreover, it's not obvious to me how one would go about obtaining "unbiased" corpora without somehow relying on subjective societal values that are different
by wolfgke 10y ago
> Moreover, it's not obvious to me how one would go about obtaining "unbiased" corpora without somehow relying on subjective societal values that are different everywhere and continually evolving.
I don't believe that problem will ever be completely solvable. But I think the road to go is to make these assumptions always explicit. I.e. when the machine learning system derives a result, program it to additionally return a proof of how it came to this result. And also give a way to let the ML system return a list of all axioms and derivation rules that it has currently learned, so that they can independently be checked how much they are biased and can thus be corrected.
- nl 10y agoIt's pretty hard to return "rules" for a ML system, especially a non-linear system. Google is currently working systems that use a trillion features - I can't imagine returning some kind of rule list for that. LIME[1] is a nice start, though. [1] https://www.oreilly.com/learning/introduction-to-local-interpretable-model-agnostic-explanations-lime https://www.oreilly.com/learning/introduction-to-local-inter...
- wolfgke 10y agoAnother text about interpreting convolutional neural networks: http://cs231n.github.io/understanding-cnn/ http://cs231n.github.io/understanding-cnn/ > Google is currently working systems that use a trillion features - I can't imagine returning some kind of rule list for that. As I wrote: It would already help if the ML system as a first step returned the derivation with only the rules that were concretely used for a concrete derivation - this list is much shorter and can thus much easier be checked.