4 ms·

> just because you own a pickup truck then you must be conservative Could you point us to whatever sentence in the article that made or even suggested this? I

> just because you own a pickup truck then you must be conservative Could you point us to whatever sentence in the article that made or even suggested this? I didn't see any part of the article stating that everyone who has a pickup truck is conservative.

nodesocket 9y ago
> For instance, if the number of sedans in a neighborhood is greater than the number of pickups, there is an 88 percent chance that the precinct will vote Democratic. Transpose those numbers to have more pickups than sedans and there is an 82 percent chance a precinct will vote Republican.
- throwaway613834 9y ago
  How do you deduce "Everyone with a pickup truck is conservative/Republican." from the statement "If P(sedans) > P(pickups) then P(precinct voting Democrat) >= 88%." ?
  - deleted 9y ago
    [deleted]
- ubernostrum 9y ago
  You know that phrase "machine learning is money laundering for bias"? You just learned what it means.
  - barry-cotter 9y ago
    You mean statistics are on average true?
    - ubernostrum 9y ago
      Or that if you feed data which contains clear indicators of bias/prejudice into your ML system, it will discover those biases and prejudices and learn and incorporate them. Got a training set which dates to when banks redlined black people? Congratulations, the model just learned not to make loans to black people! And now it's "objective", because no humans are involved in making its decisions!
      - barry-cotter 9y ago
        If you define an objective function based on some set of predictors, none of which contains a Black variable then it is not possible for your model to discriminate against Black people because it doesn’t know if anyone is black. Assuming, ridiculously conservatively that redlining ended in 1979 any dataset will have 37 non redlined years in it, 1980-2017. Given that the further away in time a datapoint is the less weight it’s given in predicting anything redlining will have bigger all impact on any model. Asian people in the US on average have higher credit scores than white people. This is not anti-white discrimination because white and Asian are not used to determine credit score, it’s based on on-time payment, capacity used, length of credit history, types of credit used and past credit applications. There are many wonderful MOOCs and textbooks on ML, AI and statistics. If you want to understand how statistical modelling works I can recommend the Georgia TechX MicroMasters in Analytics. Don’t listen to statistically illiterate journalists.
        pdkl95 9y ago
        > redlining ended Redlining is only one of the many racists housing practices that have existed and continue to exist today.l > in 1979 Have you even done any research on this topic? From a famous article[1] about the history of racism in housing: >> In 2011, Bank of America agreed to pay $355 million to settle charges of discrimination against its Countrywide unit. The following year, Wells Fargo settled its discrimination suit for more than $175 million. But the damage had been done. In 2009, half the properties in Baltimore whose owners had been granted loans by Wells Fargo between 2005 and 2008 were vacant; 71 percent of these properties were in predominantly black neighborhoods. > 37 non redlined years Those are also years of sub-standard schools, poor job prospects, and (in some cities) toxic levels of lead paint. Growing up in that kind of environment can limit your options severely. > it is not possible for your model to discriminate against Black people because it doesn’t know if anyone is black. I always like to assume ignorance over malice, so I will simply suggest that you should do a lot more research before making this kind of claim. [1] https://www.theatlantic.com/magazine/archive/2014/06/the-case-for-reparations/361631/ https://www.theatlantic.com/magazine/archive/2014/06/the-cas...
  - throwaway613834 9y ago
    > You know that phrase "machine learning is money laundering for bias"? Hadn't heard of it. Sounds catchy though. Could you explain the similarity? (Along with the definition of "bias" you're using?)
  - closeparen 9y ago
    ML algorithms are only going to have evidence-based biases (although I guess with a motivated choice of training data you can make the evidence have whatever bias you want)
    - pdkl95 9y ago
      > with a motivated choice of training data Intent is not required. > only going to have evidence-based biases That includes historic institutional biases. For example, many US cities are still highly segregated by race. This was done intentionally as a workaround for forced-integration rulings with indirect methods such as redlining, blockbusting, and restrictive covenants. While most of these methods were eventually banned, the results of those methods still exist today[1]. Since school funding usually depends on property taxes, these policies also impacted the school system. It may be a fact that one group of people performs worse on some metric, but how much of that was caused by racist housing practices decades earlier? Unless you're accounting for all of the embedded biases already present in the facts, you are laundering those biases into your results. [1] https://www.huffingtonpost.com/entry/the-9-most-segregated-cities-in-america_us_55df53e9e4b0e7117ba92d7f https://www.huffingtonpost.com/entry/the-9-most-segregated-c...
      - closeparen 9y ago
        People building classifiers are not typically trying to make a causal argument, they just want the classifier.
        pdkl95 9y ago
        Classifying anything related to people is a political act; it isn't possible to be apolitical and "just make a classifier". The classifier either accounts for the biases already present in the data, or they are present in the output, obscured (laundered) behind an opaque wall of machine learning.
        closeparen 9y ago
        Unjust policies laundered those biases into reality, at which point they stop being biases and start being facts with unjust causes. Whitewashing the training data to make predictions based on a hypothetical injustice-free world where redlining never happened will just give you garbage predictions.
- function_seven 9y ago
  That’s a description of reality. 88% of neighborhoods that had more sedans than pickups lean blue. I don’t see why that would be offensive? Seems in line with each group’s general culture.

nodesocket 9y ago

> For instance, if the number of sedans in a neighborhood is greater than the number of pickups, there is an 88 percent chance that the precinct will vote Democratic. Transpose those numbers to have more pickups than sedans and there is an 82 percent chance a precinct will vote Republican.

throwaway613834 9y ago
How do you deduce "Everyone with a pickup truck is conservative/Republican." from the statement "If P(sedans) > P(pickups) then P(precinct voting Democrat) >= 88%." ?
- deleted 9y ago
  [deleted]
ubernostrum 9y ago
You know that phrase "machine learning is money laundering for bias"? You just learned what it means.
- barry-cotter 9y ago
  You mean statistics are on average true?
  - ubernostrum 9y ago
    Or that if you feed data which contains clear indicators of bias/prejudice into your ML system, it will discover those biases and prejudices and learn and incorporate them. Got a training set which dates to when banks redlined black people? Congratulations, the model just learned not to make loans to black people! And now it's "objective", because no humans are involved in making its decisions!
    - barry-cotter 9y ago
      If you define an objective function based on some set of predictors, none of which contains a Black variable then it is not possible for your model to discriminate against Black people because it doesn’t know if anyone is black. Assuming, ridiculously conservatively that redlining ended in 1979 any dataset will have 37 non redlined years in it, 1980-2017. Given that the further away in time a datapoint is the less weight it’s given in predicting anything redlining will have bigger all impact on any model. Asian people in the US on average have higher credit scores than white people. This is not anti-white discrimination because white and Asian are not used to determine credit score, it’s based on on-time payment, capacity used, length of credit history, types of credit used and past credit applications. There are many wonderful MOOCs and textbooks on ML, AI and statistics. If you want to understand how statistical modelling works I can recommend the Georgia TechX MicroMasters in Analytics. Don’t listen to statistically illiterate journalists.
      - pdkl95 9y ago
        > redlining ended Redlining is only one of the many racists housing practices that have existed and continue to exist today.l > in 1979 Have you even done any research on this topic? From a famous article[1] about the history of racism in housing: >> In 2011, Bank of America agreed to pay $355 million to settle charges of discrimination against its Countrywide unit. The following year, Wells Fargo settled its discrimination suit for more than $175 million. But the damage had been done. In 2009, half the properties in Baltimore whose owners had been granted loans by Wells Fargo between 2005 and 2008 were vacant; 71 percent of these properties were in predominantly black neighborhoods. > 37 non redlined years Those are also years of sub-standard schools, poor job prospects, and (in some cities) toxic levels of lead paint. Growing up in that kind of environment can limit your options severely. > it is not possible for your model to discriminate against Black people because it doesn’t know if anyone is black. I always like to assume ignorance over malice, so I will simply suggest that you should do a lot more research before making this kind of claim. [1] https://www.theatlantic.com/magazine/archive/2014/06/the-case-for-reparations/361631/ https://www.theatlantic.com/magazine/archive/2014/06/the-cas...
- throwaway613834 9y ago
  > You know that phrase "machine learning is money laundering for bias"? Hadn't heard of it. Sounds catchy though. Could you explain the similarity? (Along with the definition of "bias" you're using?)
- closeparen 9y ago
  ML algorithms are only going to have evidence-based biases (although I guess with a motivated choice of training data you can make the evidence have whatever bias you want)
  - pdkl95 9y ago
    > with a motivated choice of training data Intent is not required. > only going to have evidence-based biases That includes historic institutional biases. For example, many US cities are still highly segregated by race. This was done intentionally as a workaround for forced-integration rulings with indirect methods such as redlining, blockbusting, and restrictive covenants. While most of these methods were eventually banned, the results of those methods still exist today[1]. Since school funding usually depends on property taxes, these policies also impacted the school system. It may be a fact that one group of people performs worse on some metric, but how much of that was caused by racist housing practices decades earlier? Unless you're accounting for all of the embedded biases already present in the facts, you are laundering those biases into your results. [1] https://www.huffingtonpost.com/entry/the-9-most-segregated-cities-in-america_us_55df53e9e4b0e7117ba92d7f https://www.huffingtonpost.com/entry/the-9-most-segregated-c...
    - closeparen 9y ago
      People building classifiers are not typically trying to make a causal argument, they just want the classifier.
      - pdkl95 9y ago
        Classifying anything related to people is a political act; it isn't possible to be apolitical and "just make a classifier". The classifier either accounts for the biases already present in the data, or they are present in the output, obscured (laundered) behind an opaque wall of machine learning.
        closeparen 9y ago
        Unjust policies laundered those biases into reality, at which point they stop being biases and start being facts with unjust causes. Whitewashing the training data to make predictions based on a hypothetical injustice-free world where redlining never happened will just give you garbage predictions.
function_seven 9y ago
That’s a description of reality. 88% of neighborhoods that had more sedans than pickups lean blue. I don’t see why that would be offensive? Seems in line with each group’s general culture.