8 ms·

Boston housing price dataset was removed from scikit-learn 1.2

81 points by ok123456 4y ago

Der_Einzige 4y ago
Good riddance. Even independent of the ethical problems, we should use harder benchmark datasets than this or iris.
josephcsible 4y ago
Am I understanding this right? They purged an entire dataset because one of its independent variables is how integrated a neighborhood is?
- version_five 4y ago
  Yes. It's an embarrassment.
- mcs5280 4y ago
  If we selectively hide things from view all the world's problems will go away
  - mistrial9 4y ago
    .. with AI
  - JeremyBanks 4y ago
    My Rights to be included as example data in a tool distribution
  - mertd 4y ago
    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 4y ago
      Putting unnecessarily gates in front making the process non-standard is not the opposite of hiding.. perhaps somewhere inbetween
      - laidoffamazon 4y ago
        They suggest an alternative of the same type without these issues.
  - tshaddox 4y ago
    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 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.
- ffssffss 4y ago
  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.
  - josephcsible 4y ago
    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?
    - anigbrowl 4y ago
      [flagged]
      - trinsic2 4y ago
        Stop what?
        anigbrowl 4y ago
        Expressing incredulity over one piece of context without acknowledging the existence of other parts which modify it. There was a full detailed explanation at the medium blog, and all that was necessary to do was follow the citation given and read it. Repeatedly proclaiming confusion when already in possession of the explanatory material, rather than challenging that explanation, is not a credible posture.
- threeseed 4y ago
  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 http://lib.stat.cmu.edu/datasets/boston
  - josephcsible 4y ago
    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 4y ago
      [deleted]
    - threeseed 4y ago
      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 4y ago
        > 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?
  - 4y ago
- anigbrowl 4y ago
  No, you are not. https://medium.com/@docintangible/racist-data-destruction-113e3eff54a8 https://medium.com/@docintangible/racist-data-destruction-11...
- loeg 4y ago
  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.
  - josephcsible 4y ago
    > 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. Isn't the point of that operation that a 1% black neighborhood and a 99% black neighborhood are both less integrated than a 50% black neighborhood? If you didn't do something like squaring, then wouldn't at least one of the former incorrectly register as more integrated than the latter? > 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. Agreed.
    - loeg 4y ago
      Yeah, I mean, I guess I'd suggest a raw Bk column to preserve the original data and maybe just absolute value of difference instead of square of difference.
- mcguire 4y ago
  From https://fairlearn.org/main/user_guide/datasets/boston_housing_data.html https://fairlearn.org/main/user_guide/datasets/boston_housin...: "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 4y ago
    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.
  - space_fountain 4y ago
    A confusion I sometimes have is where we should start pulling the string to unravel systematic racism. On some levels it can seem like if the purpose of this dataset was to analyze the impact of these variables on the prices humans (who were and are racist) are willing to pay than if B had predictive power (because people were racist), than excluding it in general seems wrong. On the other hand applying that across society is part of how we got redlining and in general a lot of the systematic part of systematic racism. Should we sort of handicap our models of human behavior to exclude race (and try to exclude proxies the model can find). I think regardless removing this model seems right since it's A a toy, B dated, and C liable to not be seen with the care it needs to be, but say if I'm trying to calculate who will be the next famous actor, is it wrong to include variables to allow the model to pick up on how shallow and say fat phobic society is (or just plain racist again)
erehweb 4y ago
Some discussion of the dataset and issues - this link claims it was removed in June 2020 https://fairlearn.org/main/user_guide/datasets/boston_housing_data.html https://fairlearn.org/main/user_guide/datasets/boston_housin...
- VHRanger 4y ago
  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
  - pxmpxm 4y ago
    This - it's really encoding the authors' model with some magic values in there. Not even ethics, just bad stats. The correct step would be back out the raw proportion of black population, rather than the output of that quadratic model. Edit: D'oh 62% and 64% black neighborhoods get the same B. Couple glasses of wine in already, math is hard...
pb060 4y ago
Thanks god. Most boring dataset ever. The reason why I never got past the first chapter of any ML book I tried to read.
loehnsberg 4y ago
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 4y ago
  Can you really call this "banning the dataset"? https://github.com/scikit-learn/scikit-learn/commit/8a86e2196f2bb8de65f4b8231feeca43a0b5e0b4 https://github.com/scikit-learn/scikit-learn/commit/8a86e219...
  - duskwuff 4y ago
    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.
- kristianp 4y ago
  Does it make sense to revise a definition of a real world data series to some random definition like "birds by town"?
aftbit 4y ago
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 4y ago
  You can read the full reason in the commit diff: https://github.com/scikit-learn/scikit-learn/commit/8a86e2196f2bb8de65f4b8231feeca43a0b5e0b4 https://github.com/scikit-learn/scikit-learn/commit/8a86e219...
  - version_five 4y ago
    Only on HN would someone ask for an ELI5 and get pointed to a git commit diff :)
    - guipsp 4y ago
      You are, of course, correct. I gave it a better shot.
- guipsp 4y ago
  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.
  - 1attice 4y ago
    Great explanation but consider adding blurbs about what makes a feature artificial, what 'invertibility' is, why it's important, and how you could (as you put it) train one's way out of the bias so long as the feature is invertible. Finally, bring it home by giving an example (fictious or, even better, real-historical!) of a model that would be biased, but for the blessings of feature-invertibility and further training, and then explain that you since you couldn't do that with this dataset, because the artificial feature is not invertible. That I think gets us a full ELI5, though I agree the common-sense cutoff is subjective. (And I say this in a spirit of co-collaboration -- I'm fascinated by the problem of ELI5 something like this, as I wind up having these conversations ad-hoc in-the-wild with family and friends, as I work in the field. Finding simple language just is progress)
civilized 4y ago
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. Contrary to the cited Medium post, including race, integration, or racism-related factors as predictors of housing prices doesn't imply that it's okay for those drivers to exist in reality, or that the model is suitable for some real-world deployment where it might affect real prices. That is not the only use case; such a model could just as easily be used to imply that those factors are bad. Such a model could play a role in a disparate impact case fighting against racism. I don't love how the dataset creators only included B and not the underlying untransformed value, and I agree that it's based on a questionable theory about how integration affects housing prices. These issues could be taken as sufficient cause to stop using the dataset, especially when better alternatives are available. But calling them ethical issues seems either puzzling or wrong. Problematizing something should ideally come with an accessible public explanation of why it is problematic. Ethics should not be obscure.
- anigbrowl 4y ago
  Kids with an interest in data science are going to read this and find it baffling, and the references won't help much. There's nothing baffling about it.
  - civilized 4y ago
    The cited Medium post says many things. Most of them seem to be background or diversions. I did my best to pick out the essential points and discuss them in my comment above. To the extent I could understand, those essential points seemed questionable. For what it's worth, another commenter posted this and I found it much clearer, if still imperfect: https://fairlearn.org/main/user_guide/datasets/boston_housing_data.html https://fairlearn.org/main/user_guide/datasets/boston_housin... The FairLearn authors here explain, like I did above, that the ethical issues depend on use case, and are not inherent to the data or the dataset developers' choice of transformation for the B variable.

81 points by ok123456 4y ago

Good riddance. Even independent of the ethical problems, we should use harder benchmark datasets than this or iris.

Am I understanding this right? They purged an entire dataset because one of its independent variables is how integrated a neighborhood is?

version_five 4y ago
Yes. It's an embarrassment.
mcs5280 4y ago
If we selectively hide things from view all the world's problems will go away
- mistrial9 4y ago
  .. with AI
- JeremyBanks 4y ago
  My Rights to be included as example data in a tool distribution
- mertd 4y ago
  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 4y ago
    Putting unnecessarily gates in front making the process non-standard is not the opposite of hiding.. perhaps somewhere inbetween
    - laidoffamazon 4y ago
      They suggest an alternative of the same type without these issues.
- tshaddox 4y ago
  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 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.
ffssffss 4y ago
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.
- josephcsible 4y ago
  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?
  - anigbrowl 4y ago
    [flagged]
    - trinsic2 4y ago
      Stop what?
      - anigbrowl 4y ago
        Expressing incredulity over one piece of context without acknowledging the existence of other parts which modify it. There was a full detailed explanation at the medium blog, and all that was necessary to do was follow the citation given and read it. Repeatedly proclaiming confusion when already in possession of the explanatory material, rather than challenging that explanation, is not a credible posture.
threeseed 4y ago
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 http://lib.stat.cmu.edu/datasets/boston
- josephcsible 4y ago
  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 4y ago
    [deleted]
  - threeseed 4y ago
    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 4y ago
      > 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?
- 4y ago
anigbrowl 4y ago
No, you are not. https://medium.com/@docintangible/racist-data-destruction-113e3eff54a8 https://medium.com/@docintangible/racist-data-destruction-11...
loeg 4y ago
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.
- josephcsible 4y ago
  > 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. Isn't the point of that operation that a 1% black neighborhood and a 99% black neighborhood are both less integrated than a 50% black neighborhood? If you didn't do something like squaring, then wouldn't at least one of the former incorrectly register as more integrated than the latter? > 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. Agreed.
  - loeg 4y ago
    Yeah, I mean, I guess I'd suggest a raw Bk column to preserve the original data and maybe just absolute value of difference instead of square of difference.
mcguire 4y ago
From https://fairlearn.org/main/user_guide/datasets/boston_housing_data.html https://fairlearn.org/main/user_guide/datasets/boston_housin...: "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 4y ago
  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.
- space_fountain 4y ago
  A confusion I sometimes have is where we should start pulling the string to unravel systematic racism. On some levels it can seem like if the purpose of this dataset was to analyze the impact of these variables on the prices humans (who were and are racist) are willing to pay than if B had predictive power (because people were racist), than excluding it in general seems wrong. On the other hand applying that across society is part of how we got redlining and in general a lot of the systematic part of systematic racism. Should we sort of handicap our models of human behavior to exclude race (and try to exclude proxies the model can find). I think regardless removing this model seems right since it's A a toy, B dated, and C liable to not be seen with the care it needs to be, but say if I'm trying to calculate who will be the next famous actor, is it wrong to include variables to allow the model to pick up on how shallow and say fat phobic society is (or just plain racist again)

erehweb 4y ago

Some discussion of the dataset and issues - this link claims it was removed in June 2020 https://fairlearn.org/main/user_guide/datasets/boston_housing_data.html https://fairlearn.org/main/user_guide/datasets/boston_housin...

VHRanger 4y ago
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
- pxmpxm 4y ago
  This - it's really encoding the authors' model with some magic values in there. Not even ethics, just bad stats. The correct step would be back out the raw proportion of black population, rather than the output of that quadratic model. Edit: D'oh 62% and 64% black neighborhoods get the same B. Couple glasses of wine in already, math is hard...

pb060 4y ago

Thanks god. Most boring dataset ever. The reason why I never got past the first chapter of any ML book I tried to read.

loehnsberg 4y ago

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 4y ago
Can you really call this "banning the dataset"? https://github.com/scikit-learn/scikit-learn/commit/8a86e2196f2bb8de65f4b8231feeca43a0b5e0b4 https://github.com/scikit-learn/scikit-learn/commit/8a86e219...
- duskwuff 4y ago
  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.
kristianp 4y ago
Does it make sense to revise a definition of a real world data series to some random definition like "birds by town"?

aftbit 4y ago

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 4y ago
You can read the full reason in the commit diff: https://github.com/scikit-learn/scikit-learn/commit/8a86e2196f2bb8de65f4b8231feeca43a0b5e0b4 https://github.com/scikit-learn/scikit-learn/commit/8a86e219...
- version_five 4y ago
  Only on HN would someone ask for an ELI5 and get pointed to a git commit diff :)
  - guipsp 4y ago
    You are, of course, correct. I gave it a better shot.
guipsp 4y ago
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.
- 1attice 4y ago
  Great explanation but consider adding blurbs about what makes a feature artificial, what 'invertibility' is, why it's important, and how you could (as you put it) train one's way out of the bias so long as the feature is invertible. Finally, bring it home by giving an example (fictious or, even better, real-historical!) of a model that would be biased, but for the blessings of feature-invertibility and further training, and then explain that you since you couldn't do that with this dataset, because the artificial feature is not invertible. That I think gets us a full ELI5, though I agree the common-sense cutoff is subjective. (And I say this in a spirit of co-collaboration -- I'm fascinated by the problem of ELI5 something like this, as I wind up having these conversations ad-hoc in-the-wild with family and friends, as I work in the field. Finding simple language just is progress)

civilized 4y ago

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. Contrary to the cited Medium post, including race, integration, or racism-related factors as predictors of housing prices doesn't imply that it's okay for those drivers to exist in reality, or that the model is suitable for some real-world deployment where it might affect real prices. That is not the only use case; such a model could just as easily be used to imply that those factors are bad. Such a model could play a role in a disparate impact case fighting against racism. I don't love how the dataset creators only included B and not the underlying untransformed value, and I agree that it's based on a questionable theory about how integration affects housing prices. These issues could be taken as sufficient cause to stop using the dataset, especially when better alternatives are available. But calling them ethical issues seems either puzzling or wrong. Problematizing something should ideally come with an accessible public explanation of why it is problematic. Ethics should not be obscure.

anigbrowl 4y ago
Kids with an interest in data science are going to read this and find it baffling, and the references won't help much. There's nothing baffling about it.
- civilized 4y ago
  The cited Medium post says many things. Most of them seem to be background or diversions. I did my best to pick out the essential points and discuss them in my comment above. To the extent I could understand, those essential points seemed questionable. For what it's worth, another commenter posted this and I found it much clearer, if still imperfect: https://fairlearn.org/main/user_guide/datasets/boston_housing_data.html https://fairlearn.org/main/user_guide/datasets/boston_housin... The FairLearn authors here explain, like I did above, that the ethical issues depend on use case, and are not inherent to the data or the dataset developers' choice of transformation for the B variable.