[scikit-learn] Replacing the Boston Housing Prices dataset
Gael Varoquaux
gael.varoquaux at normalesup.org
Fri Jul 7 01:35:05 EDT 2017
Many people gave great points in this thread, in particular Jacob's well
written email.
Andy's point about tutorials is an important one. I don't resonate at
all with Juan's message. Breaking people's code, even if it is the notes
that they use to give a lecture, is a real cost for them. The cost varies
on a case to case basis. But there are still books printed out there
that demo image processing on Lena, and these will be out for decades.
More importantly, the replacement of Lena used in scipy (the raccoon)
does not allow to demonstrate denoising properly (Lena has smooth regions
with details in the middle: the eyes), or segmentation. In effect, it has
made the examples for the ecosystem less convincing.
Of course, by definition, refusing to change anything implies that
unfortunate situations, such as discriminatory biases, cannot be fixed.
This is why changes should be considered on a case-to-case basis.
The problem that we are facing here is that a dataset about society, the
Boston housing dataset, can reveal discrimination. However, this is true
of every data about society. The classic adult data (extracted from the
American census) easily reveals income discrimination. I teach statistics
with an IQ dataset where it is easy to show a male vs female IQ
difference. This difference disappears after controlling for education
(and the purpose of my course is to teach people to control for
confounding effects).
Data about society reveals its inequalities. Not working on such data is
hiding problems, not fixing them. It is true that misuse of such data can
attempt to establish inequalities as facts of life and get them accepted.
When discussing these issues, we need to educate people about how to run
and interpret analyses.
No the Boston data will not go. No it is not a good thing to pretend that
social problems do not exist.
Gaël
On Fri, Jul 07, 2017 at 09:36:41AM +1000, Juan Nunez-Iglesias wrote:
> 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.
> 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."
> 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.
> On 7 Jul 2017, 6:36 AM +1000, Jacob Schreiber <jmschreiber91 at gmail.com>, wrote:
> Hi Tony
> 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 biases (even if they are
> not explicitly exposed to the feature).
> 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 future when the data is more serious.
> 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.
> Jacob
> On Thu, Jul 6, 2017 at 12:08 PM, Sean Violante <sean.violante at gmail.com>
> wrote:
> G Reina
> you make a bizarre argument. You argue that you should not even check
> racism as a possible factor in house prices?
> But then you yourself check whether its relevant
> Then you say
> "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"
> Which was basically what the original authors wanted to show too,
> Harrison, D. and Rubinfeld, D.L. `Hedonic prices and the demand for
> clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.
> but unless you measure ethnic make-up you cannot show that it is not a
> confounder.
> 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.
> On 6 Jul 2017 6:10 pm, "G Reina" <greina at eng.ucsd.edu> wrote:
> 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 (https://ww2.amstat.org/publications/jse/
> v19n3/decock.pdf). I am willing to submit the code change if the
> developers agree.
> 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.
> 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).
> I welcome the community's thoughts on the matter.
> Thanks.
> -Tony
> Here's an article I wrote on the Boston dataset:
> 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
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--
Gael Varoquaux
Researcher, INRIA Parietal
NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France
Phone: ++ 33-1-69-08-79-68
http://gael-varoquaux.info http://twitter.com/GaelVaroquaux
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