3 ms·

> "bias" refers to "outcomes the reporter dislikes and thinks he can generate clickbait with" That's quite a straw man for someone who claims others are trying

> "bias" refers to "outcomes the reporter dislikes and thinks he can generate clickbait with" That's quite a straw man for someone who claims others are trying to derail a conversation. > I'm explicitly advocating against this practice and in favor of using clear and precise language instead. The language you're suggesting masks the problem. It's nice that statisticians came up with a mathematical object that happens to have a politically charged name, but the conflation you're making is not an argument, and doesn't address the underlying (inherently political) problem. > In precise terms, what do you think the problem is? The precise problem is that things like skin color etc. often have strong predictive power. We've recognized this unfortunate fact -- and its societal implications -- and decided to create laws and social norms that regulate (either restrict or sometimes even require) the use of these properties in certain high-impact decisions. Unfortunately, algorithms sometimes use these features to make decisions in settings where a typical human wouldn't (or might even get into legal hot water if they did). Notice that an algorithm can exhibit this behavior without being (statistically) biased. Now, perhaps you think this problem isn't actually a problem. And you can certainly make the argument that any form of vernacular-discrimination should be OK as long as it doesn't exhibit statistical bias. But that's a separate conversation, and you can't defend that position by playing with definitions. And if you're not making that argument, then it should be clear why vernacular-bias differs from statistical-bias.

yummyfajitas 10y ago
You and pessimizer have just demonstrated pretty clearly why Techcrunch's language choices are bad. Pessimizer thinks the article is discussing inaccurate conclusions and that race realism is wrong ( https://news.ycombinator.com/item?id=13158797 https://news.ycombinator.com/item?id=13158797 ). You think race realism is accurate but we should ignore it. You both read the same article but drew totally different conclusions due to it's imprecise language. Many examples in the article don't fit your description of the problem at all (e.g. difficulties in image processing, Tay the trolled chatbot, filter bubbles), so I don't think you are correctly describing what the article means by "bias" either. I can't figure it out either; the best I can come up with is "AI-related things that make the author have negative feelings". I didn't argue in favor of or against your mysterious "vernacular-discrimination" - how could I when I don't even know what it is? The only thing that I argued is that the imprecise language used by Techcrunch is confusing people and that this is bad.
- smallnamespace 10y ago
  You seem to be missing a key point, which is that the definitions of racial bias are and have always been contentious and up for debate. The definitions have always been in flux and have evolved heavily over time. This is basically true for many politically contentious topics, as different parties repeatedly try to redefine and reframe terms to their advantage. If you insist on precise language, then we basically can never have any conversation about race, racial bias, or any other topic where the terminology hasn't been completely nailed down.
  - yummyfajitas 10y ago
    The only thing I'm advocating for in this thread is clear thinking and clear communication - in particular I oppose overloading a perfectly well defined and clear statistical term. Similarly, I advocate against new-age uses of the word "energy" - energy is a specific conserved quantity, not some mysterious unobservable thing that emanates from crystals. What benefit do we gain when other people can't even understand the words we use? It's almost as if you want to use a Motte-and-Bailey argument to confuse people ( http://slatestarcodex.com/2014/11/03/all-in-all-another-brick-in-the-motte/ http://slatestarcodex.com/2014/11/03/all-in-all-another-bric... ). Which is of course something that people do on this very topic: https://www.chrisstucchio.com/blog/2016/propublica_is_lying.html https://www.chrisstucchio.com/blog/2016/propublica_is_lying....
    - throwaway729 10y ago
      > in particular I oppose overloading a perfectly well defined and clear statistical term. The overloading happened the other way around... > What benefit do we gain when other people can't even understand the words we use? Similarly, what benefit do we gain when we use a term of art in settings where most of the audience doesn't have the technical background to be quite sure what they're even discussing? Also, there is a notion that's distinct from statistical bias and that is highly political and controversial. Today we can say "bias" and most everyone will know we're talking about that topic. Whatever word or phrase we use in place of "bias" will be just as muddy and ill-defined. And probably some mathematician will come along and build some incomplete model and name if after that new word. And then we'll have this conversation again. The problem you're trying to avoid is unavoidable.

yummyfajitas 10y ago

You and pessimizer have just demonstrated pretty clearly why Techcrunch's language choices are bad. Pessimizer thinks the article is discussing inaccurate conclusions and that race realism is wrong ( https://news.ycombinator.com/item?id=13158797 https://news.ycombinator.com/item?id=13158797 ). You think race realism is accurate but we should ignore it. You both read the same article but drew totally different conclusions due to it's imprecise language. Many examples in the article don't fit your description of the problem at all (e.g. difficulties in image processing, Tay the trolled chatbot, filter bubbles), so I don't think you are correctly describing what the article means by "bias" either. I can't figure it out either; the best I can come up with is "AI-related things that make the author have negative feelings". I didn't argue in favor of or against your mysterious "vernacular-discrimination" - how could I when I don't even know what it is? The only thing that I argued is that the imprecise language used by Techcrunch is confusing people and that this is bad.

smallnamespace 10y ago
You seem to be missing a key point, which is that the definitions of racial bias are and have always been contentious and up for debate. The definitions have always been in flux and have evolved heavily over time. This is basically true for many politically contentious topics, as different parties repeatedly try to redefine and reframe terms to their advantage. If you insist on precise language, then we basically can never have any conversation about race, racial bias, or any other topic where the terminology hasn't been completely nailed down.
- yummyfajitas 10y ago
  The only thing I'm advocating for in this thread is clear thinking and clear communication - in particular I oppose overloading a perfectly well defined and clear statistical term. Similarly, I advocate against new-age uses of the word "energy" - energy is a specific conserved quantity, not some mysterious unobservable thing that emanates from crystals. What benefit do we gain when other people can't even understand the words we use? It's almost as if you want to use a Motte-and-Bailey argument to confuse people ( http://slatestarcodex.com/2014/11/03/all-in-all-another-brick-in-the-motte/ http://slatestarcodex.com/2014/11/03/all-in-all-another-bric... ). Which is of course something that people do on this very topic: https://www.chrisstucchio.com/blog/2016/propublica_is_lying.html https://www.chrisstucchio.com/blog/2016/propublica_is_lying....
  - throwaway729 10y ago
    > in particular I oppose overloading a perfectly well defined and clear statistical term. The overloading happened the other way around... > What benefit do we gain when other people can't even understand the words we use? Similarly, what benefit do we gain when we use a term of art in settings where most of the audience doesn't have the technical background to be quite sure what they're even discussing? Also, there is a notion that's distinct from statistical bias and that is highly political and controversial. Today we can say "bias" and most everyone will know we're talking about that topic. Whatever word or phrase we use in place of "bias" will be just as muddy and ill-defined. And probably some mathematician will come along and build some incomplete model and name if after that new word. And then we'll have this conversation again. The problem you're trying to avoid is unavoidable.