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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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    2. Quentin Hardy‏Verified account @qhardy 18 Nov 2018
      Replying to @stevesi @zeynep @benedictevans

      One can also write a supra program that looks at results and tags, and reports anomalies, or things society deems are biases. Frequently, the problem with ML may be the mirror it holds up to us.

      1 reply 0 retweets 4 likes
    3. zeynep tufekci‏Verified account @zeynep 18 Nov 2018
      Replying to @qhardy @stevesi @benedictevans

      Only for obvious variables like race/gender which we know to look for. That's why there is so much reporting on that. Familiar ground. But ML will detect and discriminate things we could not previously detect, will not even think to check for. No variable list to run against.

      1 reply 3 retweets 22 likes
    4. This Tweet is unavailable.
    5. zeynep tufekci‏Verified account @zeynep 18 Nov 2018
      Replying to @stevesi @qhardy @benedictevans

      I am not going to convince you over Twitter that ML is not adding more variables, in the classic sense. 🤷‍♀️It's just not the same as say, adding more variables like heart rate, blood pressure, this and that measurement and running a regression or applying a formula.

      2 replies 0 retweets 4 likes
    6. Benedict Evans‏Verified account @benedictevans 18 Nov 2018
      Replying to @zeynep @stevesi @qhardy

      Perhaps. But simply asserting something and then ignoring any possible objection does not make for a strong position.

      1 reply 0 retweets 0 likes
    7. zeynep tufekci‏Verified account @zeynep 18 Nov 2018
      Replying to @benedictevans @stevesi @qhardy

      So that I'm clear: is the assertion you are objecting to this:"ML is different than trad programs or databases and that it creates (for computation) unique challenges to transparency and auditing?" It's good for me to understand because, honestly, I'd have ranked that as mundane.

      2 replies 0 retweets 0 likes
    8. Benedict Evans‏Verified account @benedictevans 18 Nov 2018
      Replying to @zeynep @stevesi @qhardy

      That’s a truism. The problem is that you can also say this about any new technology. Everything is always different in some important way, and so, really, it’s not a new problem at all. There are now a dozen or two replies to you pointing this out in various ways. 🤷🏻‍♂️

      1 reply 0 retweets 1 like
    9. zeynep tufekci‏Verified account @zeynep 18 Nov 2018
      Replying to @benedictevans @stevesi @qhardy

      I actually don't think so, but thanks for the clarification. Yes, I'm asserting that there is something qualitatively different about ML than any other new technology—it's not just that it's opaque (or seemingly-magical) to non-experts. It's intrinsically opaque to its experts.+

      1 reply 0 retweets 13 likes
    10. zeynep tufekci‏Verified account @zeynep 18 Nov 2018
      Replying to @zeynep @benedictevans and

      zeynep tufekci Retweeted Where the Tweets have no name

      One (1) counter is that we'll eventually crack this, and have interpretable ML (See: https://twitter.com/andrewthesmart/status/1064341779816767488 …). Other counter (2) is that we have used black-box technologies before (that it produced behavior we wanted but we did not know how). (1) maybe. (2) not too many.

      zeynep tufekci added,

      Where the Tweets have no name @andrewthesmart
      Replying to @stevesi @zeynep and 2 others
      There is an entire new sub-field of machine learning research called "interpretable machine learning" which tries to develop techniques for interpreting the mathematical structures models use to make predictions **because the models are not comprehensible**
      1 reply 0 retweets 4 likes
      zeynep tufekci‏Verified account @zeynep 18 Nov 2018
      Replying to @zeynep @benedictevans and

      But thanks, it's clearer to me what the disagreement is. I'll see if anyone has a long-form version of what I'm asserting (obviously, I think correctly) and what other historical black-box examples could compare (and also set out their limits).

      6:24 PM - 18 Nov 2018
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      0 replies 0 retweets 11 likes
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        2. Cedric Chin‏ @ejames_c 18 Nov 2018
          Replying to @stevesi @benedictevans

          Perhaps @zeynep needs to be more specific: she means Neural Networks instead of ML? Because as I understand it, ML encompasses a broad range of techniques, many of them explainable. Whereas NNs are considered not explainable by even the experts in the field.

          1 reply 0 retweets 0 likes
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