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<p>And more to the point the discussion on Reddit:</p>
<p>Â
<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">Â 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">Â Â 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>
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