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    <p>And more to the point the discussion on Reddit:</p>
    <p> 
<a class="moz-txt-link-freetext" href="https://www.reddit.com/r/MachineLearning/comments/6m8tp0/p_deep_learning_for_estimating_race_and_ethnicity/">https://www.reddit.com/r/MachineLearning/comments/6m8tp0/p_deep_learning_for_estimating_race_and_ethnicity/</a><br>
    </p>
    Bill<br>
    <br>
    <div class="moz-cite-prefix">On 7/9/17 5:13 PM, Bill Ross wrote:<br>
    </div>
    <blockquote type="cite"
      cite="mid:65531062-c9d6-ce7f-b712-0a1abd3cd935@cgl.ucsf.edu">
      <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
      <p>Possibly of interest:</p>
      <p><span style="color: rgb(36, 41, 46); font-family:
          -apple-system, system-ui, "Segoe UI", Helvetica,
          Arial, sans-serif, "Apple Color Emoji", "Segoe
          UI Emoji", "Segoe UI Symbol"; font-size: 16px;
          font-style: normal; font-variant-ligatures: normal;
          font-variant-caps: normal; font-weight: normal;
          letter-spacing: normal; orphans: 2; text-align: start;
          text-indent: 0px; text-transform: none; white-space: normal;
          widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px;
          background-color: rgb(255, 255, 255); text-decoration-style:
          initial; text-decoration-color: initial; display: inline
          !important; float: none;">Race and ethnicity Imputation from
          Disease history with Deep LEarning</span></p>
      <p><a class="moz-txt-link-freetext"
          href="https://github.com/jisungk/riddle"
          moz-do-not-send="true">https://github.com/jisungk/riddle</a><br>
      </p>
      Bill<br>
      <br>
      <div class="moz-cite-prefix">On 7/6/17 6:00 PM, Bill Ross wrote:<br>
      </div>
      <blockquote type="cite"
        cite="mid:32b9ea32-b5dc-dfbe-04ca-36e8db30160e@cgl.ucsf.edu">Unless
        the data concretely promotes discrimination, it seems
        discriminatory to exclude it. <br>
        <br>
        Bill <br>
        <br>
        On 7/6/17 5:39 PM, Sebastian Raschka wrote: <br>
        <blockquote type="cite">I think there can be some middle ground.
          I.e., adding a new, simple dataset to demonstrate regression
          (maybe autmpg, wine quality, or sth like that) and use that
          for the scikit-learn examples in the main documentation etc
          but leave the boston dataset in the code base for now. Whether
          it's a weak argument or not, it would be quite destructive to
          remove the dataset altogether in the next version or so, not
          only because old tutorials use it but many unit tests in many
          different projects depend on it. I think it might be better to
          phase it out by having a good alternative first, and I am sure
          that the scikit-learn maintainers wouldn't have anything
          against it if someone would update the examples/tutorials with
          the use of different datasets <br>
          <br>
          Best, <br>
          Sebastian <br>
          <br>
          <blockquote type="cite">On Jul 6, 2017, at 7:36 PM, Juan
            Nunez-Iglesias <a class="moz-txt-link-rfc2396E"
              href="mailto:jni.soma@gmail.com" moz-do-not-send="true"><jni.soma@gmail.com></a>
            wrote: <br>
            <br>
            For what it's worth: I'm sympathetic to the argument that
            you can't fix the problem if you don't measure it, but I
            agree with Tony that "many tutorials use it" is an extremely
            weak argument. We removed Lena from scikit-image because it
            was the right thing to do. I very much doubt that Boston
            house prices is in more widespread use than Lena was in
            image processing. <br>
            <br>
            You can argue about whether or not it's morally right or
            wrong to include the dataset. I see merit to both arguments.
            But "too many tutorials use it" is very similar in flavour
            to "the economy of the South would collapse without
            slavery." <br>
            <br>
            Regarding fair uses of the feature, I would hope that all
            sklearn tutorials using the dataset mention such uses. The
            potential for abuse and misinterpretation is enormous. <br>
            <br>
            On 7 Jul 2017, 6:36 AM +1000, Jacob Schreiber <a
              class="moz-txt-link-rfc2396E"
              href="mailto:jmschreiber91@gmail.com"
              moz-do-not-send="true"><jmschreiber91@gmail.com></a>,
            wrote: <br>
            <blockquote type="cite">Hi Tony <br>
              <br>
              As others have pointed out, I think that you may be
              misunderstanding the purpose of that "feature." We are in
              agreement that discrimination against protected classes is
              not OK, and that even outside complying with the law one
              should avoid discrimination, in model building or
              elsewhere. However, I disagree that one does this by
              eliminating from all datasets any feature that may allude
              to these protected classes. As Andreas pointed out, there
              is a growing effort to ensure that machine learning models
              are fair and benefit the common good (such as FATML, DSSG,
              etc..), and from my understanding the general consensus
              isn't necessarily that simply eliminating the feature is
              sufficient. I think we are in agreement that naively
              learning a model over a feature set containing
              questionable features and calling it a day is not okay,
              but as others have pointed out, having these features
              present and handling them appropriately can help guard
              against the model implicitly learning unfair! <br>
            </blockquote>
          </blockquote>
        </blockquote>
         ! <br>
        <blockquote type="cite">  biases (e <br>
            ven if they are not explicitly exposed to the feature). <br>
          <blockquote type="cite">
            <blockquote type="cite">I would welcome the addition of the
              Ames dataset to the ones supported by sklearn, but I'm not
              convinced that the Boston dataset should be removed. As
              Andreas pointed out, there is a benefit to having
              canonical examples present so that beginners can easily
              follow along with the many tutorials that have been
              written using them. As Sean points out, the paper itself
              is trying to pull out the connection between house price
              and clean air in the presence of possible confounding
              variables. In a more general sense, saying that a feature
              shouldn't be there because a simple linear regression is
              unaffected by the results is a bit odd because it is very
              common for datasets to include irrelevant features, and
              handling them appropriately is important. In addition, one
              could argue that having this type of issue arise in a toy
              dataset has a benefit because it exposes these types of
              issues to those learning data science earlier on and
              allows them to keep these issues in mind in the futur! <br>
            </blockquote>
          </blockquote>
        </blockquote>
        e! <br>
        <blockquote type="cite">   when the <br>
             data is more serious. <br>
          <blockquote type="cite">
            <blockquote type="cite">It is important for us all to keep
              issues of fairness in mind when it comes to data science.
              I'm glad that you're speaking out in favor of fairness and
              trying to bring attention to it. <br>
              <br>
              Jacob <br>
              <br>
              On Thu, Jul 6, 2017 at 12:08 PM, Sean Violante <a
                class="moz-txt-link-rfc2396E"
                href="mailto:sean.violante@gmail.com"
                moz-do-not-send="true"><sean.violante@gmail.com></a>
              wrote: <br>
              G Reina <br>
              you make a bizarre argument. You argue that you should not
              even check racism as a possible factor in house prices? <br>
              <br>
              But then you yourself check whether its relevant <br>
              Then you say <br>
              <br>
              "but I'd argue that it's more due to the location (near
              water, near businesses, near restaurants, near parks and
              recreation) than to the ethnic makeup" <br>
              <br>
              Which  was basically what  the original authors wanted to
              show too, <br>
              <br>
              Harrison, D. and Rubinfeld, D.L. `Hedonic prices and the
              demand for clean air', J. Environ. Economics &
              Management, vol.5, 81-102, 1978. <br>
              <br>
                but unless you measure ethnic make-up you cannot show
              that it is not a confounder. <br>
              <br>
              The term "white flight" refers to affluent white families
              moving to the suburbs.. And clearly a question is
              whether/how much was racism or avoiding air pollution. <br>
              <br>
              <br>
              <br>
              <br>
              <br>
              On 6 Jul 2017 6:10 pm, "G Reina" <a
                class="moz-txt-link-rfc2396E"
                href="mailto:greina@eng.ucsd.edu" moz-do-not-send="true"><greina@eng.ucsd.edu></a>
              wrote: <br>
              I'd like to request that the "Boston Housing Prices"
              dataset in sklearn (sklearn.datasets.load_boston) be
              replaced with the "Ames Housing Prices" dataset (<a
                class="moz-txt-link-freetext"
                href="https://ww2.amstat.org/publications/jse/v19n3/decock.pdf"
                moz-do-not-send="true">https://ww2.amstat.org/publications/jse/v19n3/decock.pdf</a>).
              I am willing to submit the code change if the developers
              agree. <br>
              <br>
              The Boston dataset has the feature "Bk is the proportion
              of blacks in town". It is an incredibly racist "feature"
              to include in any dataset. I think is beneath us as data
              scientists. <br>
              <br>
              I submit that the Ames dataset is a viable alternative for
              learning regression. The author has shown that the dataset
              is a more robust replacement for Boston. Ames is a 2011
              regression dataset on housing prices and has more than 5
              times the amount of training examples with over 7 times as
              many features (none of which are morally questionable). <br>
              <br>
              I welcome the community's thoughts on the matter. <br>
              <br>
              Thanks. <br>
              -Tony <br>
              <br>
              Here's an article I wrote on the Boston dataset: <br>
              <a class="moz-txt-link-freetext"
href="https://www.linkedin.com/pulse/hidden-racism-data-science-g-anthony-reina?trk=v-feed&lipi=urn%3Ali%3Apage%3Ad_flagship3_feed%3Bmu67f2GSzj5xHMpSD6M00A%3D%3D"
                moz-do-not-send="true">https://www.linkedin.com/pulse/hidden-racism-data-science-g-anthony-reina?trk=v-feed&lipi=urn%3Ali%3Apage%3Ad_flagship3_feed%3Bmu67f2GSzj5xHMpSD6M00A%3D%3D</a>
              <br>
              <br>
              <br>
              _______________________________________________ <br>
              scikit-learn mailing list <br>
              <a class="moz-txt-link-abbreviated"
                href="mailto:scikit-learn@python.org"
                moz-do-not-send="true">scikit-learn@python.org</a> <br>
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                moz-do-not-send="true">https://mail.python.org/mailman/listinfo/scikit-learn</a>
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