6 ms·

Predictive value isn’t an end in itself.

jessaustin 9y ago
That very much depends on one's interests. In today's modern economy, many are encouraged to tie their interests to lots of events that haven't happened yet. They prioritize predictions.
- rspeer 9y ago
  That still doesn't mean that your training data is right for the predictions you're going to make. Here's an example detached from any sort of politics. If you are training a language model, the maximum-likelihood model is one where the frequency of every word is the frequency that you observed that word, and the frequency of unseen words is 0. On your training data, you'll measure the maximum-likelihood model as making more accurate predictions than any other model. But it's also useless, because when you use it on new data, it assigns an impossible probability, 0, to any sentence containing a word it's never seen. This is a bias. You can't correct it by getting more data. You correct it by being an intelligent human who knows the maximum-likelihood model is wrong, and applying a correction on top of it (smoothing). Now account for the bias (observed by Arvind Narayanan) that all your training data is in the past, and all the predictions you want to make are in the future, but circumstances change between the past and the future.
  - JoeAltmaier 9y ago
    I think that's false. A model may never reduce any probability to zero. Novel data is a normal feature of modelling.
    - rspeer 9y ago
      > A model may never reduce any probability to zero. That's the entire point. You can't teach a model not to reduce probabilities to zero just by showing it more data. There will always be more unseen data. And you, a human, know that there will always be more data. So you apply smoothing to the model so that your model isn't nonsense. I'm giving an example of a bias that you can't solve with data, that you must solve with design.
    - nl 9y ago
      To be specific, what is the probability that the next word is "sdh7777asljashd_"? Any word level model trained on any dataset will say it is zero. But that is incorrect; humans know that there is always a probability that some arbitrary sequence of characters will suddenly appear in text (think a GUID in technical documentation). To account for this, the model's author has to make sure to smooth the output so it doesn't produce these zero probabilities. No amount of data can ever fix this problem (although more data can put better bounds on the smoothing factor).
      - JoeAltmaier 9y ago
        And I'm saying, depends on how the model is built. If it is a simple dictionary then yes. But there are better ways to build a model. Its a naïve view that all models are like some strawman.
        nl 9y ago
        I don't understand what you are arguing. >>> the maximum-likelihood model is one where the frequency of every word is the frequency that you observed that word, and the frequency of unseen words is 0 >> I think that's false. A model may never reduce any probability to zero. Novel data is a normal feature of modelling. > But there are better ways to build a model. Yes of course there are. That's exactly what the OP said ("You correct it by being an intelligent human who knows the maximum-likelihood model is wrong, and applying a correction on top of it") but you seemed to be arguing against it. (And to be clear, there are also alternatives to the maximum likelihood model)
  - haberman 9y ago
    When is a model ever evaluated based on its ability to predict the training data? That is classic overfitting. This is why the training set is kept separate from test/validation sets. https://en.wikipedia.org/wiki/Training,_test,_and_validation_sets https://en.wikipedia.org/wiki/Training,_test,_and_validation...
    - nl 9y ago
      I think you missed the point here. The author is showing exactly why this is a big problem in NLP, beyond the test/train split issue. Even with test/train splits you still need to smooth your model's predictions because it will always get unseen words. That's just the nature of word based lanugage models.
    - rspeer 9y ago
      Everyone capable of understanding this thread knows what overfitting is, and the difference between training and test data. That is the topic we are talking about. Because test data is unseen at training time, it cannot possibly affect the way your model is trained. The test data is not how you prevent overfitting, it's how you measure overfitting. To prevent overfitting, you need to design features such as smoothing into your model.
      - haberman 9y ago
        I think your point was a straw man. You can tell if your model is good by measuring its predictive power on data outside your training set. You seemed to be arguing that only way to know the model is wrong is to insert a human who just knows a priori that the model is wrong and what "correction" to apply. But I think this is a false analysis. You can measure a model's accuracy by seeing how well it predicts data outside the training set. If the model is highly predictive, you have a good model. It doesn't take a human's subjective analysis or principled intervention to determine whether the model is accurate with respect to the available data. This article concerns cases where the model is admittedly accurate (with respect to the available data), but is subjectively considered objectionable. That is a totally different issue.
gliese1337 9y ago
But it's a convergent instrumental goal for just about every end goal you could think of for a statistical model. If your model has zero predictive power, and that's fine, then you don't need a model in the first place. You can just make random guesses, or assume everything is equally correlated, or whatever make sense for your goal.
joshuamorton 9y ago
Well it is, it's just by no means the only one.
gowld 9y ago
Predicitive value is the primary end of modelling. What's not an end in itself, as the fine article explains, is simplistically globally maximizing predictive power with reductive recommendations. This leads to tyranny of the majority, as the model optimizes only for majority viewpoints because they have more weight, while failing to serve minority users -- and as the number of dimensions increase, the number of people who are "majority" in all dimensions is a minority of users being well served! So models must not be "one size fits all", but should allow a measure of personalization, so that in a world where most people prefer dogs to cats, cat lovers can still get content they enjoy.
maldeh 9y ago
Hit the nail on the nose. Sure, you can build a discriminating classifier or generative model that is the most accurate, correctly identifying / emulating the reality of our world down to 5 nines; and nobody says you shouldn't be able to identify or quantify all of these "inconvenient" associations. The trouble is always when you intend to make decisions based on your framework -- the decision to offer somebody a loan, the choice of language your chatbot uses, etc. -- and that is where fairness ought to be paramount. And yes, if you manage to build a discriminating model that sneaks in protected classes through indirect causal effects with no attempt to suppress them, it would yield your insurance agency higher returns over time, and just because that's the way the world is, currently. But all this would achieve is perpetuating the current status quo, placing short-term gain ahead of long-term equality. You might be doing right by yourself, but that still makes you morally impugnable. One shouldn't need to wait for an overreaching law prohibiting such indirection, to do the right thing.
- haberman 9y ago
  If a person is loan-worthy, offering them a loan is good business sense. If others are denying loans for no good reason, that is a business opportunity. It doesn't require benevolence to offer people loans that they are qualified for. How far do you expect systems to go to ignore real-world associations? What if it is coming down to personal safety? Would you object to a self-driving car that routes itself around more dangerous neighborhoods? What about a model that predicts that unknown men on the street at night are more dangerous than unknown women?
  - trjordan 9y ago
    One problem is the world has layered effects of bias. Imagine a simple system where people are only evaluated for credit cards and mortgages, and businesses can only offer one of these services. Some people are systematically denied credit cards. Now they have to have more cash for daily needs, which makes them objectively worse as home owners, so the mortgage loaners pick up on this fact and start systematically denying them mortgages. There's no business opportunity here. For either lender, the prospects in this group are worse, only because the other lenders think so. Breaking the barricade is much harder than just starting a new lender or either type.

That very much depends on one's interests. In today's modern economy, many are encouraged to tie their interests to lots of events that haven't happened yet. They prioritize predictions.

rspeer 9y ago
That still doesn't mean that your training data is right for the predictions you're going to make. Here's an example detached from any sort of politics. If you are training a language model, the maximum-likelihood model is one where the frequency of every word is the frequency that you observed that word, and the frequency of unseen words is 0. On your training data, you'll measure the maximum-likelihood model as making more accurate predictions than any other model. But it's also useless, because when you use it on new data, it assigns an impossible probability, 0, to any sentence containing a word it's never seen. This is a bias. You can't correct it by getting more data. You correct it by being an intelligent human who knows the maximum-likelihood model is wrong, and applying a correction on top of it (smoothing). Now account for the bias (observed by Arvind Narayanan) that all your training data is in the past, and all the predictions you want to make are in the future, but circumstances change between the past and the future.
- JoeAltmaier 9y ago
  I think that's false. A model may never reduce any probability to zero. Novel data is a normal feature of modelling.
  - rspeer 9y ago
    > A model may never reduce any probability to zero. That's the entire point. You can't teach a model not to reduce probabilities to zero just by showing it more data. There will always be more unseen data. And you, a human, know that there will always be more data. So you apply smoothing to the model so that your model isn't nonsense. I'm giving an example of a bias that you can't solve with data, that you must solve with design.
  - nl 9y ago
    To be specific, what is the probability that the next word is "sdh7777asljashd_"? Any word level model trained on any dataset will say it is zero. But that is incorrect; humans know that there is always a probability that some arbitrary sequence of characters will suddenly appear in text (think a GUID in technical documentation). To account for this, the model's author has to make sure to smooth the output so it doesn't produce these zero probabilities. No amount of data can ever fix this problem (although more data can put better bounds on the smoothing factor).
    - JoeAltmaier 9y ago
      And I'm saying, depends on how the model is built. If it is a simple dictionary then yes. But there are better ways to build a model. Its a naïve view that all models are like some strawman.
      - nl 9y ago
        I don't understand what you are arguing. >>> the maximum-likelihood model is one where the frequency of every word is the frequency that you observed that word, and the frequency of unseen words is 0 >> I think that's false. A model may never reduce any probability to zero. Novel data is a normal feature of modelling. > But there are better ways to build a model. Yes of course there are. That's exactly what the OP said ("You correct it by being an intelligent human who knows the maximum-likelihood model is wrong, and applying a correction on top of it") but you seemed to be arguing against it. (And to be clear, there are also alternatives to the maximum likelihood model)
- haberman 9y ago
  When is a model ever evaluated based on its ability to predict the training data? That is classic overfitting. This is why the training set is kept separate from test/validation sets. https://en.wikipedia.org/wiki/Training,_test,_and_validation_sets https://en.wikipedia.org/wiki/Training,_test,_and_validation...
  - nl 9y ago
    I think you missed the point here. The author is showing exactly why this is a big problem in NLP, beyond the test/train split issue. Even with test/train splits you still need to smooth your model's predictions because it will always get unseen words. That's just the nature of word based lanugage models.
  - rspeer 9y ago
    Everyone capable of understanding this thread knows what overfitting is, and the difference between training and test data. That is the topic we are talking about. Because test data is unseen at training time, it cannot possibly affect the way your model is trained. The test data is not how you prevent overfitting, it's how you measure overfitting. To prevent overfitting, you need to design features such as smoothing into your model.
    - haberman 9y ago
      I think your point was a straw man. You can tell if your model is good by measuring its predictive power on data outside your training set. You seemed to be arguing that only way to know the model is wrong is to insert a human who just knows a priori that the model is wrong and what "correction" to apply. But I think this is a false analysis. You can measure a model's accuracy by seeing how well it predicts data outside the training set. If the model is highly predictive, you have a good model. It doesn't take a human's subjective analysis or principled intervention to determine whether the model is accurate with respect to the available data. This article concerns cases where the model is admittedly accurate (with respect to the available data), but is subjectively considered objectionable. That is a totally different issue.

gliese1337 9y ago

But it's a convergent instrumental goal for just about every end goal you could think of for a statistical model. If your model has zero predictive power, and that's fine, then you don't need a model in the first place. You can just make random guesses, or assume everything is equally correlated, or whatever make sense for your goal.

joshuamorton 9y ago

Well it is, it's just by no means the only one.

gowld 9y ago

Predicitive value is the primary end of modelling. What's not an end in itself, as the fine article explains, is simplistically globally maximizing predictive power with reductive recommendations. This leads to tyranny of the majority, as the model optimizes only for majority viewpoints because they have more weight, while failing to serve minority users -- and as the number of dimensions increase, the number of people who are "majority" in all dimensions is a minority of users being well served! So models must not be "one size fits all", but should allow a measure of personalization, so that in a world where most people prefer dogs to cats, cat lovers can still get content they enjoy.

maldeh 9y ago

Hit the nail on the nose. Sure, you can build a discriminating classifier or generative model that is the most accurate, correctly identifying / emulating the reality of our world down to 5 nines; and nobody says you shouldn't be able to identify or quantify all of these "inconvenient" associations. The trouble is always when you intend to make decisions based on your framework -- the decision to offer somebody a loan, the choice of language your chatbot uses, etc. -- and that is where fairness ought to be paramount. And yes, if you manage to build a discriminating model that sneaks in protected classes through indirect causal effects with no attempt to suppress them, it would yield your insurance agency higher returns over time, and just because that's the way the world is, currently. But all this would achieve is perpetuating the current status quo, placing short-term gain ahead of long-term equality. You might be doing right by yourself, but that still makes you morally impugnable. One shouldn't need to wait for an overreaching law prohibiting such indirection, to do the right thing.

haberman 9y ago
If a person is loan-worthy, offering them a loan is good business sense. If others are denying loans for no good reason, that is a business opportunity. It doesn't require benevolence to offer people loans that they are qualified for. How far do you expect systems to go to ignore real-world associations? What if it is coming down to personal safety? Would you object to a self-driving car that routes itself around more dangerous neighborhoods? What about a model that predicts that unknown men on the street at night are more dangerous than unknown women?
- trjordan 9y ago
  One problem is the world has layered effects of bias. Imagine a simple system where people are only evaluated for credit cards and mortgages, and businesses can only offer one of these services. Some people are systematically denied credit cards. Now they have to have more cash for daily needs, which makes them objectively worse as home owners, so the mortgage loaners pick up on this fact and start systematically denying them mortgages. There's no business opportunity here. For either lender, the prospects in this group are worse, only because the other lenders think so. Breaking the barricade is much harder than just starting a new lender or either type.