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Maybe it is, maybe it isn't. But this is not relevant to what I'm saying. The goal of the AI is not to associate words in a hypothetical world where there is no

Maybe it is, maybe it isn't. But this is not relevant to what I'm saying. The goal of the AI is not to associate words in a hypothetical world where there is no discrimination or bias. The goal is to build a model that accurately reflects the real world. Again, there may be justifications to deliberately introduce bias in the model to produce higher rates of association between women and technology than would normally occur if the creators of the AI so desire. But this is not elimination of bias, this is deliberate introduction of bias to make th model match the creators' worldviews.

crooked-v 7y ago
AI does not, in and of itself, have a goal. AI exists to be used for some human's goal. If the human's goal is to understand the level of association between women and technology without confounding factors like decades of marketing video games and computers to boys more than girls, the historical bias of such factors in the data fed to the AI becomes extremely important.
gbrown 7y ago
> The goal is to build a model that accurately reflects the real world. This is incredibly reductive - we use models for MANY purposes - prediction, inference, description, evaluation. Until you outline a specific use case for the model, understand where the data its trained on comes from and how it may or may not reflect reality, and think about whether the phenomena which you're measuring are important/sufficient for your application you CANNOT possibly justify your broad claims.
- manfredo 7y ago
  I'm well aware that models are used for a variety of applications. But regardless of the application, we usually want the model to reflect reality in some way. Say you have an AI that predicts probability of re-offending for prospective parolees. If we observe that this system is flagging men as high risk more frequently than women, then this is not indicative of bias. But!, one might say, men are socialized to be more violent and are more frequently recruited by gangs. These factors are being absorbed by the AI. Yes, they are, and this does not make the AI biased. The job of the AI to predict risk of re-offending in real world conditions. Not risk of re-offending in a hypothetical world where everyone has the same environment and experiences. If we were to manually adjust the model to make it display equal risks for both men and women then that is not elimination of bias. It is deliberate introduction of bias to make the output fit our ideal worldview.
  - crooked-v 7y ago
    > If we observe that this system is flagging men as high risk more frequently than women, then this is not indicative of bias. If all it's doing is associating being a man with a higher risk factor without taking into account anything else in that man's life then, yes, it's being biased. "Being a man" in this situation is a correlation to, not casuation of, actual factors making a person more likely to commit crime. This is a real problem that has been happening for years in Florida: the automated risk assessments used for a variety of decisions, including bail and sentencing factors, labels black people as inherently being more likely to reoffend than white people, even after controlling for actual crime rates and recidivism rates. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing https://www.propublica.org/article/machine-bias-risk-assessm...
    - manfredo 7y ago
      It depends on the crime. But for crimes like, say, sexual assault being male is indeed a huge factor in likelihood to offend. Disparities between rates of women committing sexual assault and rates of men is immense - one or two orders of magnitude depending on the exact crimes counted. I agree that an AI shouldn't use race or gender as an input. But we should not surprised when AIs have disparities when predicting risk of committing crimes, when there are disparities in the rates of actually committing crimes.
    - losvedir 7y ago
      That propublica article is the perfect example of the many aspects of the discussion here. It argues just one side of it, but the scenario it raises is one where an ML algorithm correctly found a solution to a badly framed problem. Briefly, (and simplifying for clarity) it worked like this: the ML algorithm scored the risk of criminals reoffending. It gave a "high risk" score if someone was 80% likely to re-offend, and a "low risk" score if they had a 20% chance. Out of 100 blacks and 100 whites, here are the scores the ML algorithm gave, and how many ultimately did re-offend: 100 black criminals * 50 "high risk", 40 re-offended * 50 "low risk", 10 re-offended 100 white criminals * 10 "high risk", 8 re-offended * 90 "low risk", 18 re-offended On the one hand, the algorithm was completely correct: 80% of high risk individuals re-offended, and 20% of low risk individuals did, and this is true even looking at just the black or the white criminals. It was unbiased according to its goals. On the other hand, the failure mode disproportionately punished the black criminals: 10 were "high risk" who never re-offended, while only 2 white criminals were. Meanwhile, 18 "low risk" white criminals did re-offend while only 10 black criminals did. So the score was more strict for some unlucky blacks and more lenient for some lucky whites. However, a key point is that because the underlying re-offense rates were different between the populations (50/100 for the black criminals, 26/100 for the white criminals), the algorithm could not have done otherwise. That is, given some n% re-offenders, if you have to fit them into 20% and 80% buckets, your "high risk" and "low risk" counts are mathematically fixed. In other words, it wasn't the ML by itself that was the problem, but the COMPAS score that it was trying to compute that had these issues inherent in it. I think this is a good example where ML wasn't biased (at least, not anymore than reality), but where people were too eager to turn to it for a poorly considered project. By wrapping up important questions in high tech algorithms, it's too easy to fool yourself that what you're doing is the best thing you could be doing and miss problems in the fundamental framing.
loopz 7y ago
This is a common misunderstanding. When models impact the world, what matters more are their outcomes and consequences.
- manfredo 7y ago
  Right. And when you force a model to avoid disparities when disparities really do exist, the outcome is inaccurate data and the consequence is that decisions are made based on inaccurate data.
  - loopz 7y ago
    Sure, and there is also the assumption that good decisions are made on perfect/good data, which is also just an assumption. We can't even define good decisions well enough to be sure. Just because bad data may tend (in hindsight) to yield inaccurate decisions, or decisions based on faulty data, doesn't promote data as the main driver behind sound decisions. That would just be another belief system, and we don't have all possible data or omniscience to prove it!
    - manfredo 7y ago
      No, you can test and gauge the quality of that data. At this point, you're not really even making a point about whether or not we should introduce biases in our models to make them match our worldview - you're saying the entire endeavor of trying to build a model is worthless. If what you're saying is true, that we can't measure what decisions are successful or unsuccessful, then there's no point in trying to build a model because we no way of knowing whether the model is working or not.
      - loopz 7y ago
        It is the claim of vendors that their models works. It is irresponsible to just take their word for it, and such tests and critique need be ongoing.
  - alexknvl 7y ago

crooked-v 7y ago

AI does not, in and of itself, have a goal. AI exists to be used for some human's goal. If the human's goal is to understand the level of association between women and technology without confounding factors like decades of marketing video games and computers to boys more than girls, the historical bias of such factors in the data fed to the AI becomes extremely important.

gbrown 7y ago

> The goal is to build a model that accurately reflects the real world. This is incredibly reductive - we use models for MANY purposes - prediction, inference, description, evaluation. Until you outline a specific use case for the model, understand where the data its trained on comes from and how it may or may not reflect reality, and think about whether the phenomena which you're measuring are important/sufficient for your application you CANNOT possibly justify your broad claims.

manfredo 7y ago
I'm well aware that models are used for a variety of applications. But regardless of the application, we usually want the model to reflect reality in some way. Say you have an AI that predicts probability of re-offending for prospective parolees. If we observe that this system is flagging men as high risk more frequently than women, then this is not indicative of bias. But!, one might say, men are socialized to be more violent and are more frequently recruited by gangs. These factors are being absorbed by the AI. Yes, they are, and this does not make the AI biased. The job of the AI to predict risk of re-offending in real world conditions. Not risk of re-offending in a hypothetical world where everyone has the same environment and experiences. If we were to manually adjust the model to make it display equal risks for both men and women then that is not elimination of bias. It is deliberate introduction of bias to make the output fit our ideal worldview.
- crooked-v 7y ago
  > If we observe that this system is flagging men as high risk more frequently than women, then this is not indicative of bias. If all it's doing is associating being a man with a higher risk factor without taking into account anything else in that man's life then, yes, it's being biased. "Being a man" in this situation is a correlation to, not casuation of, actual factors making a person more likely to commit crime. This is a real problem that has been happening for years in Florida: the automated risk assessments used for a variety of decisions, including bail and sentencing factors, labels black people as inherently being more likely to reoffend than white people, even after controlling for actual crime rates and recidivism rates. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing https://www.propublica.org/article/machine-bias-risk-assessm...
  - manfredo 7y ago
    It depends on the crime. But for crimes like, say, sexual assault being male is indeed a huge factor in likelihood to offend. Disparities between rates of women committing sexual assault and rates of men is immense - one or two orders of magnitude depending on the exact crimes counted. I agree that an AI shouldn't use race or gender as an input. But we should not surprised when AIs have disparities when predicting risk of committing crimes, when there are disparities in the rates of actually committing crimes.
  - losvedir 7y ago
    That propublica article is the perfect example of the many aspects of the discussion here. It argues just one side of it, but the scenario it raises is one where an ML algorithm correctly found a solution to a badly framed problem. Briefly, (and simplifying for clarity) it worked like this: the ML algorithm scored the risk of criminals reoffending. It gave a "high risk" score if someone was 80% likely to re-offend, and a "low risk" score if they had a 20% chance. Out of 100 blacks and 100 whites, here are the scores the ML algorithm gave, and how many ultimately did re-offend: 100 black criminals * 50 "high risk", 40 re-offended * 50 "low risk", 10 re-offended 100 white criminals * 10 "high risk", 8 re-offended * 90 "low risk", 18 re-offended On the one hand, the algorithm was completely correct: 80% of high risk individuals re-offended, and 20% of low risk individuals did, and this is true even looking at just the black or the white criminals. It was unbiased according to its goals. On the other hand, the failure mode disproportionately punished the black criminals: 10 were "high risk" who never re-offended, while only 2 white criminals were. Meanwhile, 18 "low risk" white criminals did re-offend while only 10 black criminals did. So the score was more strict for some unlucky blacks and more lenient for some lucky whites. However, a key point is that because the underlying re-offense rates were different between the populations (50/100 for the black criminals, 26/100 for the white criminals), the algorithm could not have done otherwise. That is, given some n% re-offenders, if you have to fit them into 20% and 80% buckets, your "high risk" and "low risk" counts are mathematically fixed. In other words, it wasn't the ML by itself that was the problem, but the COMPAS score that it was trying to compute that had these issues inherent in it. I think this is a good example where ML wasn't biased (at least, not anymore than reality), but where people were too eager to turn to it for a poorly considered project. By wrapping up important questions in high tech algorithms, it's too easy to fool yourself that what you're doing is the best thing you could be doing and miss problems in the fundamental framing.

loopz 7y ago

This is a common misunderstanding. When models impact the world, what matters more are their outcomes and consequences.

manfredo 7y ago
Right. And when you force a model to avoid disparities when disparities really do exist, the outcome is inaccurate data and the consequence is that decisions are made based on inaccurate data.
- loopz 7y ago
  Sure, and there is also the assumption that good decisions are made on perfect/good data, which is also just an assumption. We can't even define good decisions well enough to be sure. Just because bad data may tend (in hindsight) to yield inaccurate decisions, or decisions based on faulty data, doesn't promote data as the main driver behind sound decisions. That would just be another belief system, and we don't have all possible data or omniscience to prove it!
  - manfredo 7y ago
    No, you can test and gauge the quality of that data. At this point, you're not really even making a point about whether or not we should introduce biases in our models to make them match our worldview - you're saying the entire endeavor of trying to build a model is worthless. If what you're saying is true, that we can't measure what decisions are successful or unsuccessful, then there's no point in trying to build a model because we no way of knowing whether the model is working or not.
    - loopz 7y ago
      It is the claim of vendors that their models works. It is irresponsible to just take their word for it, and such tests and critique need be ongoing.
- alexknvl 7y ago