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AI fairness is really, really bogus as a research field. Understanding and reducing negative impacts of bias is important, but the current research field of AI

by mlthoughts2018 6y ago

AI fairness is really, really bogus as a research field. Understanding and reducing negative impacts of bias is important, but the current research field of AI fairness does not do anything like that and has not yet reached any state of maturity where it can be considered a serious subset of research at all. I would really say even one mention of it on a list like this is purely to do virtue signalling. It’s the same for “explainability” of models too, another totally bogus field that gets treated as being worthy of attention or societal prioritization purely due to politics.

currymj 6y ago
it is very strange to read someone claiming with extreme confidence that all fairness and explainability research is completely bogus. of course there are a lot of papers of questionable value, but that's a problem with machine learning more broadly -- probably all academic science, really. lots of institutions (banks, medicine, etc.) really do need explainable models. in practice this often means linear models with simple coefficients or shallow decision trees. there is plenty of useful work on learning these while maintaining performance, or "distilling" them from more complicated models. i know for a fact some explainable models learned with these techniques do actually get used in real life. likewise with fairness -- end-users actually do care about fairer models in all kinds of areas, especially lending and insurance. there's a ton of frustrating debate about how to operationalize fairness in different settings but it seems like there is actually progress on this front. what do you find to be bogus about these research areas?
- mlthoughts2018 6y ago
  Linear models with simple coefficients can often be some of the least explainable models, particularly when the assumption of linearity breaks down. [0] is a good classic paper on this, demonstrating a simple example where coding error on the inputs leads to erroneous coefficient estimates that are both statistically significant and also of the wrong sign. Meaning, you would believe the coefficients reflect a real relationship between the covariate and the target, and even could claim it’s statistically significant, and yet the actual relationship to the target is of the opposite sign! Any further feature importance scoring based off the coefficient estimates would then become catastrophically misleading. Meanwhile, a model like support vector regression on the same data is capable of automatically handling the non-linearity, at the expense that there’s no more such thing as a coefficient breakdown in the linear space of input features. Does this make it less “explainable”? That would make zero sense. How can it be worse at explaining a data generating mechanism when it is better at predicting that same generating mechanism. What could it possibly mean to explain something you can’t predict or replicate? The field of “explainable” models doesn’t even make the slightest attempt to address this stuff - it just beats up on models that are arbitrarily labeled as “black boxes” (what does that mean?) If a given model X predicts or replicates a data generating process better than Y, then X explains the process better than Y, period. An analogy: Newtonian physics is not “more explainable than” quantum mechanics. Newtonian physics is just more wrong about how the world works than quantum mechanics. [0]: http://www.saramitchell.org/achen04.pdf http://www.saramitchell.org/achen04.pdf
  - currymj 6y ago
    i think "explainable model" literally means "a model you can explain to people". You may have to explain your decision to a judge or customer after the fact, or you might even be asking a layperson to actually compute predictions manually (as is sometimes done in psychology and medicine for things like triage). the question of whether or not the model provides a good explanation for the data-generating process is a distinct one. I think the Achen paper makes a good point that people cannot safely turn linear regression coefficients into stories about the world, although it doesn't seem like they've stopped trying. but assuming you have a way to validate that it makes good predictions (not an unreasonable assumption), an explainable model can be a useful thing to have.
    - mlthoughts2018 6y ago
      You’re totally missing the point. “I can explain how this model works” is not something you can claim about a model. You can only claim it about a tuple of (model, assumptions, data, context). In context A, some simple linear regression might be very “explainable.” In context B, that same linear model might be totally not explainable (because the mechanism of coefficients based on the regression’s fitting procedure might be totally incompatible with other details of the situation.) The analogy between Newtonian and quantum mechanics still holds. The fact that you can more easily map Newtonian mechanics onto English words or pictures absolutely doesn’t make it more “explainable.” By that logic, saying “a magic wizard did it” would be the most “explainable” model of all. This is exactly my point. What is “complex” or “simple”? Arbitrary standards of natural language words? The field of explainable models has done no work on this. It starts from some totally arbitrary and confused idea about something both being an accurate model and an explainable model as if they are separate. The closest academic topic to making “explainability” a serious subject would be the philosophy of language and connection to computability theory, like Kolmogorov complexity, PAC learning, VC dimension, Occam’s razor. But these are algorithmic aspects of model complexity in the face of a specific data set, it’s absolutely not some hand wavy “oh but a person could ‘easily’ understand certain verbal acoustic vibrations about this” based on nothing.
      - 6y ago
longtom 6y ago
I kinda agree that "fairness", even though a valid issue, seems receive a disproportional amount of attention. The central dangers, apart from misuse, seem to be a coordination problem and a control problem. The coordination problem is that AGI will likely lead to a winner-takes-all scenario, implying a per-emptive strike becomes rational once one player seems too far ahead of the game. The control problem is that the value function of an AGI may diverge from our own value function.

currymj 6y ago

it is very strange to read someone claiming with extreme confidence that all fairness and explainability research is completely bogus. of course there are a lot of papers of questionable value, but that's a problem with machine learning more broadly -- probably all academic science, really. lots of institutions (banks, medicine, etc.) really do need explainable models. in practice this often means linear models with simple coefficients or shallow decision trees. there is plenty of useful work on learning these while maintaining performance, or "distilling" them from more complicated models. i know for a fact some explainable models learned with these techniques do actually get used in real life. likewise with fairness -- end-users actually do care about fairer models in all kinds of areas, especially lending and insurance. there's a ton of frustrating debate about how to operationalize fairness in different settings but it seems like there is actually progress on this front. what do you find to be bogus about these research areas?

mlthoughts2018 6y ago
Linear models with simple coefficients can often be some of the least explainable models, particularly when the assumption of linearity breaks down. [0] is a good classic paper on this, demonstrating a simple example where coding error on the inputs leads to erroneous coefficient estimates that are both statistically significant and also of the wrong sign. Meaning, you would believe the coefficients reflect a real relationship between the covariate and the target, and even could claim it’s statistically significant, and yet the actual relationship to the target is of the opposite sign! Any further feature importance scoring based off the coefficient estimates would then become catastrophically misleading. Meanwhile, a model like support vector regression on the same data is capable of automatically handling the non-linearity, at the expense that there’s no more such thing as a coefficient breakdown in the linear space of input features. Does this make it less “explainable”? That would make zero sense. How can it be worse at explaining a data generating mechanism when it is better at predicting that same generating mechanism. What could it possibly mean to explain something you can’t predict or replicate? The field of “explainable” models doesn’t even make the slightest attempt to address this stuff - it just beats up on models that are arbitrarily labeled as “black boxes” (what does that mean?) If a given model X predicts or replicates a data generating process better than Y, then X explains the process better than Y, period. An analogy: Newtonian physics is not “more explainable than” quantum mechanics. Newtonian physics is just more wrong about how the world works than quantum mechanics. [0]: http://www.saramitchell.org/achen04.pdf http://www.saramitchell.org/achen04.pdf
- currymj 6y ago
  i think "explainable model" literally means "a model you can explain to people". You may have to explain your decision to a judge or customer after the fact, or you might even be asking a layperson to actually compute predictions manually (as is sometimes done in psychology and medicine for things like triage). the question of whether or not the model provides a good explanation for the data-generating process is a distinct one. I think the Achen paper makes a good point that people cannot safely turn linear regression coefficients into stories about the world, although it doesn't seem like they've stopped trying. but assuming you have a way to validate that it makes good predictions (not an unreasonable assumption), an explainable model can be a useful thing to have.
  - mlthoughts2018 6y ago
    You’re totally missing the point. “I can explain how this model works” is not something you can claim about a model. You can only claim it about a tuple of (model, assumptions, data, context). In context A, some simple linear regression might be very “explainable.” In context B, that same linear model might be totally not explainable (because the mechanism of coefficients based on the regression’s fitting procedure might be totally incompatible with other details of the situation.) The analogy between Newtonian and quantum mechanics still holds. The fact that you can more easily map Newtonian mechanics onto English words or pictures absolutely doesn’t make it more “explainable.” By that logic, saying “a magic wizard did it” would be the most “explainable” model of all. This is exactly my point. What is “complex” or “simple”? Arbitrary standards of natural language words? The field of explainable models has done no work on this. It starts from some totally arbitrary and confused idea about something both being an accurate model and an explainable model as if they are separate. The closest academic topic to making “explainability” a serious subject would be the philosophy of language and connection to computability theory, like Kolmogorov complexity, PAC learning, VC dimension, Occam’s razor. But these are algorithmic aspects of model complexity in the face of a specific data set, it’s absolutely not some hand wavy “oh but a person could ‘easily’ understand certain verbal acoustic vibrations about this” based on nothing.
    - 6y ago

longtom 6y ago

I kinda agree that "fairness", even though a valid issue, seems receive a disproportional amount of attention. The central dangers, apart from misuse, seem to be a coordination problem and a control problem. The coordination problem is that AGI will likely lead to a winner-takes-all scenario, implying a per-emptive strike becomes rational once one player seems too far ahead of the game. The control problem is that the value function of an AGI may diverge from our own value function.