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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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      zeynep tufekci‏Verified account @zeynep 23 Mar 2019
      Replying to @mims @susanthesquark @mathbabedotorg

      If by "algorithms" we mean ML—and that's what we mean in 2019—ML is inherently and intrinsically bound with data, and it's increasingly formally (formally, as in mathematically) clear that problem of bias thus isn't solveable. https://arxiv.org/abs/1903.03862  andhttps://arxiv.org/abs/1609.05807 

      5:13 PM - 23 Mar 2019
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      • Roja 🏳️‍🌈 Ian Soboroff Chen Mingi Jack Clark Meredith Broussard Rajiv Nunna Jeff Wear Mara Einstein 😷 Miles (He/Him)
      3 replies 4 retweets 55 likes
        1. New conversation
        2. zeynep tufekci‏Verified account @zeynep 23 Mar 2019
          Replying to @zeynep @mims and

          More diversity would be great for many reasons, but the problem is inherent to the method/data. Pretty clear we will need to find new technical methods and political will (and for sure, diversify the people who design systems and spot issues but the challenge is deeper).

          1 reply 1 retweet 17 likes
        3. zeynep tufekci‏Verified account @zeynep 23 Mar 2019
          Replying to @zeynep @mims and

          And let's not get even started with fair... Different definitions of fair are often in mathematical conflict (see the Kleinberg et. al paper). (And, crucially) we're not using math the way we use them to probe laws of nature.)

          1 reply 0 retweets 18 likes
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        2. Dare Obasanjo‏ @Carnage4Life 23 Mar 2019
          Replying to @zeynep @mims and

          +1. Machine learning algorithms are biased based on the data they were trained with the same way humans are biased by their experiences. There's no such thing as an unbiased human just one who isn't biased about a specific thing in a specific context. Applies similarly to ML

          1 reply 2 retweets 18 likes
        3. zeynep tufekci‏Verified account @zeynep 23 Mar 2019
          Replying to @Carnage4Life @mims and

          Yep, and, more and more solid papers showing that there is no way out for ML. Post-hoc intervention is possible, as with humans, but that's a whole different thing.

          0 replies 0 retweets 4 likes
        4. End of conversation
        1. New conversation
        2. The Variable‏ @TristanMiano 23 Mar 2019
          Replying to @zeynep @mims and

          This is why it would be nice if we didn't use the same terms for "algorithms" in the traditional sense (like quicksort, euclid's gcd method, etc) and ML, which could be thought of as "algorithm constructors", which are much trickier to reason about and predict consequences of.

          1 reply 0 retweets 3 likes
        3. Douglas Frank‏ @dpfrank07 24 Mar 2019
          Replying to @TristanMiano @zeynep and

          Was thinking same, this terminology matters and we should strive to not convolute.

          1 reply 0 retweets 0 likes
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        2. zeynep tufekci‏Verified account @zeynep 24 Mar 2019
          Replying to @stevesi @mims and

          In most cases ML 1-surfaces bias; and/or 2-focuses it via feedbackloops; and/or 3-creates new ones by adding the ability to detect things at scale that we couldn't before (not hiring people prone to depression, for example). All those are risks, though 1 is also an opportunity.

          3 replies 0 retweets 9 likes
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