[scikit-learn] Replacing the Boston Housing Prices dataset

[Bill Ross]

Unless the data concretely promotes discrimination, it seems 
discriminatory to exclude it.

Bill

On 7/6/17 5:39 PM, Sebastian Raschka wrote:
> 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
>
> Best,
> Sebastian
>
>> On Jul 6, 2017, at 7:36 PM, Juan Nunez-Iglesias <jni.soma at gmail.com> 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 (e
>   ven 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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