[HN Gopher] Boston housing price dataset was removed from scikit...
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Boston housing price dataset was removed from scikit-learn 1.2
Author : ok123456
Score : 58 points
Date : 2022-12-30 20:29 UTC (2 hours ago)
(HTM) web link (github.com)
(TXT) w3m dump (github.com)
| loehnsberg wrote:
| I used the dataset with my students, as it is small and does not
| require preprocessing, like dummy coding or handling missing
| values. Students also brought the racial issue to my attention
| and it created a bit of a discussion. We eventually decided to
| simply change the definition to "birds by town" and moved on.
|
| Think of all the children books that get rewritten. Read the new
| ones to your children and discuss the old ones when they are
| teenagers. I would have preferred if sklearn contributors had
| done the same and simply revised the description as opposed to
| removing the dataset.
|
| EDIT: changed "banning" to "removing" the dataset
| guipsp wrote:
| Can you really call this "banning the dataset"?
| https://github.com/scikit-learn/scikit-learn/commit/8a86e219...
| duskwuff wrote:
| This is an impressively responsible way of handling the
| situation, and I'd recommend that others read it as well. It
| identifies the specific problem with the dataset which led to
| its removal from the library (with references!), tells the
| user how to retrieve it if they really need it, and suggests
| alternatives.
| Der_Einzige wrote:
| Good riddance. Even independent of the ethical problems, we
| should use harder benchmark datasets than this or iris.
| aftbit wrote:
| Can someone ELI5 why this was removed? Is the problem just that
| the dataset includes a feature that references black people which
| might cause a model to draw a connection between black population
| and housing values? I thought it was pretty well accepted that
| (for a huge complex variety of historical reasons) black people
| tend to live in neighborhoods with lower valued houses. Or is
| there a deeper fairness issue that I'm missing?
| guipsp wrote:
| The biggest problem is that the dataset contains an artificial
| feature that is not invertible. This is an issue because the
| biases of the author of the dataset are present in that
| feature, and you will never be able to "train" your way out of
| it because it is not invertible.
| guipsp wrote:
| You can read the full reason in the commit diff:
| https://github.com/scikit-learn/scikit-learn/commit/8a86e219...
| version_five wrote:
| Only on HN would someone ask for an ELI5 and get pointed to a
| git commit diff :)
| guipsp wrote:
| You are, of course, correct. I gave it a better shot.
| erehweb wrote:
| Some discussion of the dataset and issues - this link claims it
| was removed in June 2020
| https://fairlearn.org/main/user_guide/datasets/boston_housin...
| VHRanger wrote:
| I'm annoyed that the dataset has the B variable and the LSAT
| variable encoding some manually-chosen hyperparameters in the
| formula creating them.
|
| If the dataset gave the raw features it would be better at
| least
| pb060 wrote:
| Thanks god. Most boring dataset ever. The reason why I never got
| past the first chapter of any ML book I tried to read.
| civilized wrote:
| What makes me uncomfortable here is the obscure description of
| the issue and how the obscurity will affect beginners and young
| minds. Kids with an interest in data science are going to read
| this and find it baffling, and the references won't help much.
| They will get the impression that ethics in machine learning is
| some sort of abstruse field that they can't reason about on their
| own, so they need to be told what is ethical by experts.
|
| The explanation also seems technically inaccurate. Including a
| squared feature like the B variable does not assume a positive
| impact of B. The impact could just as easily be negative if the
| data is more consistent with that pattern, because the regression
| coefficient on B could have either sign. If fitting the data
| yielded a negative sign, it would imply that the most integrated
| neighborhoods have the highest housing prices rather than the
| lowest.
| josephcsible wrote:
| Am I understanding this right? They purged an entire dataset
| because one of its independent variables is how integrated a
| neighborhood is?
| version_five wrote:
| Yes. It's an embarrassment.
| mcs5280 wrote:
| If we selectively hide things from view all the world's
| problems will go away
| mertd wrote:
| Read the code? You can try to import it but you'll get a
| message explaining the problems with it and a link to where
| to find it shall you decide you'd like to use it anyway. It's
| the opposite of hiding.
| ipaddr wrote:
| Putting unnecessarily gates in front making the process
| non-standard is not the opposite of hiding.. perhaps
| somewhere inbetween
| tshaddox wrote:
| This is a very small collection of toy datasets that this
| machine learning library includes for the purpose of
| illustrating the behavior of the library: http://scikit-
| learn.org/stable/datasets/toy_dataset.html
|
| It's out of the scope of this library to publish all datasets
| in existence or highlight particular datasets that are
| relevant to particular societal problems. It's literally just
| a few datasets so that you can play around with the ML
| library without downloading any external datasets. I think
| it's fair to allow them to exercise reasonable discretion in
| their choice of which toy datasets to ship with their ML
| library.
| mistrial9 wrote:
| .. with AI
| JeremyBanks wrote:
| My Rights to be included as example data in a tool
| distribution
| ffssffss wrote:
| What's the point of such an incendiary comment? No, you aren't
| understanding it right. At worst you are offering a
| deliberately misleading interpretation. Here's what the link
| says: The Boston housing prices dataset
| has an ethical problem: as investigated in [1],
| the authors of this dataset engineered a non-
| invertible variable "B" assuming that racial self-segregation
| had a positive impact on house prices [2].
| Furthermore the goal of the research that led
| to the creation of this dataset was to study the
| impact of air quality but it did not give adequate
| demonstration of the validity of this
| assumption. The scikit-learn maintainers
| therefore strongly discourage the use of this
| dataset unless the purpose of the code is to study and educate
| about ethical issues in data science and machine learning.
| trinsic2 wrote:
| How is doing a sanity check incendiary in your view again?
| ffssffss wrote:
| [flagged]
| generalizations wrote:
| > Please respond to the strongest plausible
| interpretation of what someone says, not a weaker one
| that's easier to criticize. Assume good faith.
|
| https://news.ycombinator.com/newsguidelines.html
| ffssffss wrote:
| Sadly, assumptions of good faith are easily exploited by
| bad actors (the classic term for this is "just asking
| questions") but I suppose you're right, I should not have
| assumed malice.
| generalizations wrote:
| Better one one mud slinger than two, for the sake of the
| community. Guilty of it myself too many times. Cheers.
| josephcsible wrote:
| > The non-invertability is part of the problem, and he
| completely doesn't understand that.
|
| I get that invertibility means that you can't fully
| recover the original racial percentage, e.g., that a
| 48/52 split and a 78/22 split will both look exactly the
| same, since (.48-.63)^2 and (.78-.63)^2 are equal. I
| don't see why that totally taints the entire dataset.
| europeanguy wrote:
| > I think he wrote the comment in bad faith. The non-
| invertability is part of the problem, and he completely
| doesn't understand that.
|
| If he doesn't understand it, then it's not in bad faith.
| Right?
| jeffreyrogers wrote:
| I don't see why non-invertibility matters. Lots of useful
| features are non-invertible.
|
| Edit: and if you are dealing with real data sets or
| producing real datasets for analysis you will often have
| only approximations to the thing you want to measure.
| Determining whether your proxy variable is worth
| including or how to interpret your results in light of it
| are necessary skills to develop.
| guipsp wrote:
| The feature is bad. The non-invertibility means that you
| cannot get back the original data that was used to
| generate the feature, and try to salvage it.
| josephcsible wrote:
| Sure, that makes it less useful. But why is that so bad
| that the entire dataset should be discarded and not used,
| even for uses that don't care about that particular part
| of the original data?
| jimbob45 wrote:
| Is it just me or this some horrifically bad English? I have a
| fairly strong math background and I'm struggling to figure
| out what the author meant by any of that.
|
| As far as I can tell, it's something like, "The author made a
| bad variable. Also, the goal was to check air quality but the
| variable was bad." What does the subject being air quality
| impact have to do with anything there?
| josephcsible wrote:
| The "B" variable measures how integrated a neighborhood is,
| and that snippet seems to be saying that its existence is the
| "ethical problem" that led them to purge the dataset. How is
| any of that different than what I said?
| threeseed wrote:
| Please provide a source that says that "number of blacks in
| my neighbourhood" is a measure of "neighbourhood
| integration".
|
| It's a ridiculous and offensive premise from my
| perspective.
| josephcsible wrote:
| > Please provide a source that says that "number of
| blacks in my neighbourhood" is a measure of
| "neighbourhood integration".
|
| It isn't. Bk is "number of blacks in my neighbourhood" as
| you put it, and the whole point of using B instead of it
| was so that an all-black neighborhood wouldn't count as
| more integrated than one with a mix of races.
| CoastalCoder wrote:
| > Please provide a source that says that "number of
| blacks in my neighbourhood" is a measure of
| "neighbourhood integration".
|
| I wonder if the dataset design makes more sense in the
| context of Boston in particular: [0].
|
| [0] https://en.wikipedia.org/wiki/Boston_desegregation_bu
| sing_cr...
| CoastalCoder wrote:
| > It's a ridiculous and offensive premise from my
| perspective.
|
| Can you elaborate on the problem you have with this?
|
| (I'm just trying to not guess at your meaning.)
| anigbrowl wrote:
| [flagged]
| trinsic2 wrote:
| Stop what?
| loeg wrote:
| Close, but not exactly. One of its variables is how far its
| integration differs from 63% Black, _squared_.
|
| I.e., you cannot distinguish a 73% black neighborhood from a
| 53% black neighborhood with this variable.
|
| It's a bizarre variable and I guess I could see purging the
| column or at least suggesting it not be used, but I don't
| really understand why you'd delete the rest of the (sample)
| dataset on this basis.
| mcguire wrote:
| From https://fairlearn.org/main/user_guide/datasets/boston_hous
| in...:
|
| " _Thus, any models trained using this data that do not take
| special care to process B will learn to use mathematically
| encoded racism as a factor in house price prediction._ "
| loeg wrote:
| House price prediction for prices in 1970s Boston, yes, where
| housing prices almost certainly reflected racist preferences.
| That seems like a (potentially) accurate model?
|
| ML models could also learn that the correlation between B and
| price is negative (i.e., that integration improves house
| prices). But the critics of the dataset all suggest that B
| and price are positively correlated.
| threeseed wrote:
| No. The dataset [1] defines B as:
|
| 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
|
| And not sure how anyone can argue the dataset is worthy of
| being included. It is pretty offensive and misguided at minimum
| to argue that having more black people in your neighbourhood
| will depress housing prices. And for it to be solely because
| they are black and not to do with a range of other factors e.g.
| socio-economic.
|
| [1] http://lib.stat.cmu.edu/datasets/boston
| mdcds wrote:
| data is just that, not an argument in itself. how you use it
| to disprove something is up to you.
| sidlls wrote:
| But it's a fact that--due to the very racism you're pointing
| out--house prices tend to be lower the more minorities there
| are. That's in part _because of_ the racist policies of banks
| and the real estate industry. Ignoring it doesn't do anyone a
| service. Now, whether it's used /included responsibly in this
| dataset is another matter entirely.
| mateo411 wrote:
| How did they arrive at .63?
|
| It's seems like a weird choice. If there are 62% or 64% then
| the feature will yield the same value.
|
| I think it would make more sense just to include percentage
| of households where at least one member of the household has
| a certain ethnicity.
|
| I don't think it's a offensive to analyze demographic
| information in the aggregate. In fact this happens all the
| time.
| HPsquared wrote:
| It is kind of strange, I don't understand why they would
| use that. Here's a plot of the function:
|
| https://www.wolframalpha.com/input?i=plot+%5B1000*%28x-0.63
| %...
| josephcsible wrote:
| I wonder if that was the overall proportion for the entire
| surrounding area at the time. If so, then B would be a
| measure of how different the racial makeup of a given
| subset is from the entire area.
| Yajirobe wrote:
| Data can show a correlation. What the underlying causation is
| is not what the dataset aims to answer.
| kortilla wrote:
| Offensive to who? It sounds like a great way to compare
| racism in different areas.
|
| The more it correlates with a lower price, the more race
| issues.
| version_five wrote:
| It is pretty offensive and misguided at minimum to argue that
| having more black people in your neighbourhood will depress
| housing prices.
|
| I think that's the wrong lens to look at this through. I'm
| happy to concede your statement about it being offensive is
| true (although I think from a purely statistical perspective,
| correlations with poverty, etc probably make the assumption
| correct. Before 2015 or so when we all lost it, it would only
| be racist to say there was a causal relationship between race
| and price, not a correlation). Anyway, that's all an aside.
|
| It's the purging of a dataset, a toy dataset in this context,
| for a reason of political correctness, that I don't support.
| If you look hard enough at anything, you can probably find a
| way to call it racist or some similar slur. If we start
| applying this lens to tools like scikit learn, we go down a
| path I don't agree with, that's completely performative in
| terms of actually addressing any wrongs, and is a continuing
| distraction from what could be useful work. Debating if and
| how racist this is is immaterial imo to whether or not we
| should erase everything doesn't align with modern
| hypersensitivity about political correctness
| micromacrofoot wrote:
| You're missing the fact that there are a lot of reasons the
| data itself is actually bad... another commenter shared a
| reasonable analysis:
| https://medium.com/@docintangible/racist-data-
| destruction-11...
|
| So even if you want to ignore the fact that the data was
| outright used to discriminate in the past, the data itself
| is actually flawed in several ways...
| threeseed wrote:
| > If you look hard enough at anything, you can probably
| find a way to call it racist or some similar slur.
|
| Which is such an insane premise that it's hard to take the
| rest of your point seriously.
| josephcsible wrote:
| Isn't that a measure of how integrated a neighborhood is? And
| even accepting for the sake of argument that such a variable
| is evil, why not just exclude it instead of ditching the
| whole dataset?
| [deleted]
| threeseed wrote:
| a) No. If 50% of the community is African-Americans and
| live on one half and everyone else on the other then it
| would be exactly integrated. Except of course it isn't.
|
| b) It is not scikit-learn's responsibility to alter third
| party datasets.
| CoastalCoder wrote:
| > a) No. If 50% of the community is African-Americans and
| live on one half and everyone else on the other then it
| would be exactly integrated. Except of course it isn't.
|
| IIUC, you're arguing that "whole-town" level aggregation
| is misleading. So if we get more granular, we could do it
| my neighborhood, street, building, apartment/car/shelter,
| bedroom, bed, bed @ time of day, etc.
|
| Any one of those aggregation levels could hide
| interesting distinctions that could be made if only the
| data were reported with even _more_ granularity.
|
| So are you arguing against aggregation in general? Or
| just whole-town aggregation specifically?
| thw09j9m wrote:
| > offensive and misguided
|
| The data is the data. The data isn't suggesting that "having
| more black people in your neighbourhood will depress housing
| prices." That's your take on what a racist causal
| interpretation would look like.
|
| The correlation is very real and turning a blind eye to it is
| worse: https://www.brookings.edu/testimonies/how-racial-
| disparities...
| micromacrofoot wrote:
| That's actually a racist causal interpretation made about
| the data when it was originally analyzed:
|
| > At low to moderate levels of B, an increase in B should
| have a negative influence on housing value if Blacks are
| regarded as undesirable neighbors by Whites.
| RobotToaster wrote:
| Isn't that saying that lower house prices are caused by
| racism?
| micromacrofoot wrote:
| That's actually a racist causal interpretation made about
| the data when it was originally compiled and analyzed...
| which raises a lot of alarm bells:
|
| > At low to moderate levels of B, an increase in B should
| have a negative influence on housing value if Blacks are
| regarded as undesirable neighbors by Whites.
| anigbrowl wrote:
| No, you are not. https://medium.com/@docintangible/racist-data-
| destruction-11...
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