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That's not training a model to predict the quality of edits - that's training a model to predict the reactions of admins/editors :) Perhaps a better title is

That's not training a model to predict the quality of edits - that's training a model to predict the reactions of admins/editors :) Perhaps a better title is "AI service models Wikipedia editors"?

tptacek 11y ago
That's all the model needs to do to be valuable.
- logicallee 11y ago
  Well, suppose something told me what I would decide if I gave any matter 15 seconds of consideration. It would be an invaluable tool I might use often, for example for sorting and classifying, throwing out trash and so forth. For example, it would be a fantastic spam classifier, since even after 15 seconds there is no doubt. I could use it to get through hundreds of spam messages easily, or delegate things to appropriate departments and so forth. But it would not give me "x-ray vision", since at the end the day it is the same thing I myself conclude if I give the matter 15 seconds of consideration. Something that I miss - a genuine mail written the way a spam might be ("hey! it was nice to meet you!" in the subject, etc), by a recent acquaintance I forgot I made - would be misclassified as spam. There is no x-ray vision involved here - it's not training on the data - it's training on me! I feel the distinction is an important one.
  - tptacek 11y ago
    It's 15 seconds of consideration you have to give to huge volumes of edits, so singling out the ones where that 15 seconds is likely to be profitable for an editor is extremely helpful. (I have no idea how well the scoring works in practice). Remember, the editorial tasks we're talking about here are enervating.
    - logicallee 11y ago
      fair enough, I guess I don't like the "x-ray specs" analogy. Their example is a disruptive edit[1] where a URL is replaced by LLAMAS GROW ON TREES perhaps rather than say it gives editors "x-ray specs", it would be fair to say it gives editors caffeine :) [1] - https://wikimediablog.files.wordpress.com/2015/11/diff_642215410.png https://wikimediablog.files.wordpress.com/2015/11/diff_64221...
kenrick95 11y ago
They also have a model to predict a quality of edit, based on the WP1.0 quality model that has been used as a guideline for some time. https://meta.wikimedia.org/wiki/ORES/wp10 https://meta.wikimedia.org/wiki/ORES/wp10

That's not training a model to predict the quality of edits - that's training a model to predict the reactions of admins/editors :) Perhaps a better title is "AI service models Wikipedia editors"?

tptacek 11y ago

That's all the model needs to do to be valuable.

logicallee 11y ago
Well, suppose something told me what I would decide if I gave any matter 15 seconds of consideration. It would be an invaluable tool I might use often, for example for sorting and classifying, throwing out trash and so forth. For example, it would be a fantastic spam classifier, since even after 15 seconds there is no doubt. I could use it to get through hundreds of spam messages easily, or delegate things to appropriate departments and so forth. But it would not give me "x-ray vision", since at the end the day it is the same thing I myself conclude if I give the matter 15 seconds of consideration. Something that I miss - a genuine mail written the way a spam might be ("hey! it was nice to meet you!" in the subject, etc), by a recent acquaintance I forgot I made - would be misclassified as spam. There is no x-ray vision involved here - it's not training on the data - it's training on me! I feel the distinction is an important one.
- tptacek 11y ago
  It's 15 seconds of consideration you have to give to huge volumes of edits, so singling out the ones where that 15 seconds is likely to be profitable for an editor is extremely helpful. (I have no idea how well the scoring works in practice). Remember, the editorial tasks we're talking about here are enervating.
  - logicallee 11y ago
    fair enough, I guess I don't like the "x-ray specs" analogy. Their example is a disruptive edit[1] where a URL is replaced by LLAMAS GROW ON TREES perhaps rather than say it gives editors "x-ray specs", it would be fair to say it gives editors caffeine :) [1] - https://wikimediablog.files.wordpress.com/2015/11/diff_642215410.png https://wikimediablog.files.wordpress.com/2015/11/diff_64221...

kenrick95 11y ago

They also have a model to predict a quality of edit, based on the WP1.0 quality model that has been used as a guideline for some time. https://meta.wikimedia.org/wiki/ORES/wp10 https://meta.wikimedia.org/wiki/ORES/wp10