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zeynep's profile
zeynep tufekci
zeynep tufekci
zeynep tufekci
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@zeynep

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zeynep tufekciVerified account

@zeynep

Complex systems, wicked problems. Society, technology, science and more. @UNC professor. @NYTimes columnist. My newsletter is @insight: http://www.theinsight.org 

floating in a most peculiar way
theinsight.org
Joined August 2009

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    8. Soren Larson‏ @srnlrsn 25 Mar 2019
      Replying to @mims @zeynep

      Expanding the analytic paradigm here could be helpful- suppose we could (approximately) define what bias in ML looks like from first principles. Then we might filter candidate ML decisions through this analytic filter, which is at least transparent, though perhaps limiting

      1 reply 0 retweets 0 likes
    9. zeynep tufekci‏Verified account @zeynep 25 Mar 2019
      Replying to @srnlrsn @mims

      You cannot define bias from first principles, though. It requires what you think are relevant correlates and what are not, and that is a very political decision which requires agreement.

      1 reply 0 retweets 1 like
    10. Soren Larson‏ @srnlrsn 25 Mar 2019
      Replying to @zeynep @mims

      Do not agree. "prejudice in favor of or against one thing, person, or group compared with another"- proving prejudice- personal intention- is challenging, but perhaps you adjust a definition of bias, and corresponding action you might take if it's found, given the chosen def

      1 reply 0 retweets 0 likes
      zeynep tufekci‏Verified account @zeynep 25 Mar 2019
      Replying to @srnlrsn @mims

      Good luck defining what things or groups we should not be prejiduced against from first principles. (Should a company be allowed to hire fewer introverts for a sales job? It's something ML can detect at scale actionable enough and you may not even know that's what's happening).

      5:43 AM - 25 Mar 2019
      • 1 Like
      • Christopher Mims 🤌
      1 reply 0 retweets 1 like
        1. New conversation
        2. Soren Larson‏ @srnlrsn 25 Mar 2019
          Replying to @zeynep @mims

          Unfairness is a construct left to us to decide. Fix a definition. Do you think there's not a first principles argument against preventing bias against e.g., women in workplaces? Or are we talking about semantics? ML algos can be broken apart; innards exposed as needed.

          1 reply 0 retweets 1 like
        3. zeynep tufekci‏Verified account @zeynep 25 Mar 2019
          Replying to @srnlrsn @mims

          Gender/race are mostly agreed upon, at least on the surface. Easier case. ML creates all sorts of categories of detection that we've no way of dealing with because we couldn't detect them at scale, and which ones are relevant, to be protected, to be used etc. is very thorny.

          1 reply 0 retweets 1 like
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