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It's unpopular to talk about, but there is quantifiably more crime in some minority communities in the US. There are some crimes, like drug use, that are indee

It's unpopular to talk about, but there is quantifiably more crime in some minority communities in the US. There are some crimes, like drug use, that are indeed over-reported in some communities due to over-policing. But there are other crimes, like homicide, that are nearly universally reported, regardless of where they are committed. And those crimes are much more frequent in minority-majority parts of the country. Turning a blind eye to this problem is a disservice to those communities, because they are the same communities that are most commonly the victims of crimes.

crooked-v 7y ago
> there is quantifiably more crime in some minority communities Sure... and what about the chronic lead exposure issues in many of these areas, the effects of long-term overpolicing of minor crimes on family stability, and the impact of (lack of) generational wealth and education from being used as de facto or literal slaves for generations? The 'minority' part here is a correlative factor, not a causative one.
- nostromo 7y ago
  An AI classifier doesn't know, or care, why a particular population has higher crime and lower income, it only recognizes and reports on the pattern.
  - gbrown 7y ago
    EXACTLY
    - manfredo 7y ago
      But I think you're missing the point. Deliberately altering the model to produce equal results despite unequal patterns in reality is not the elimination of bias, it's introducing bias. Is the goal of the AI model to predict crime rates in a hypothetical world where everyone has equal rates of lead exposure? Or is the goal of the model to predict crime rates in the real world?
      - crooked-v 7y ago
        It depends on what you're using it for and why. If you're concretely distributing police resources, you probably want short-term prediction of actual aggregate crime rates (but also want to consider the risk of overpolicing based on crime rates only recorded higher because of historical overpolicing). On the other hand, if you want to understand anything about the populations involved on some abstract level, merely plugging in predicted crime rates based on historical data won't help at all.
        manfredo 7y ago
        This comment chain is in reference to law enforcement and the justice system. I would hope that our law enforcement and justice systems are operating on real world data, rather than a hypothetical world's data.
        crooked-v 7y ago
        I would absolutely not want the justice system to use historical statistical information without considering factors like lead exposure and past overpolicing, and even pure boots-on-ground distributions of law enforcement resources should take into account historical over- and under-policing and its effects on the statistics being used to distribute those resources.
        manfredo 7y ago
        Okay, that's your position. Plenty of people do not want an AI that, say, gives parole to someone that is a high risk of re-offending because someone engineered the AI to be more lenient towards people who grew up in areas with greater lead exposure.
        crooked-v 7y ago
        Consider if someone grew up in an area of high lead exposure but was, one way or another, protected from it as a child [1]. Should they be treated as having the same recidivism risk factor as someone who suffered the full effects of lead exposure? If not, how do you separate the effects of lead exposure from other factors to recidivism rates? [1] See the 1999 documentary Blast from the Past.
    - nightski 7y ago
      If you already know what you want the model to say ahead of time and are tweaking it to fit that narrative then there is no point in creating a model in the first place.
  - crooked-v 7y ago
    ...which is exactly the kind of "treating culturally- and situationally-contextual results like inherent facts" bias the article is talking about.
    - DuskStar 7y ago
      And if those results change, so will the algorithm's outputs! But asking the algorithm to make the change seems to be a bit much. Honestly, my preferred method of solving this would be to train the algorithm on a data set with all of the forbidden values included along with anything else the creator feels relevant - zip code, income, familial status, favorite sport, education - and then when running in production, against real people, don't give it the restricted information. Yes, you could theoretically extract race, gender and other protected stats from the information the algorithm actually uses in prod - but it has no incentive to, since a less-noisy signal is already provided. For instance, suppose the optimal algorithm for your data set is some linear function of X, Y and Z - let's say X+Y+Z to keep things simple. X,Y and Z are all normally distributed variables, mean of 0 and the same standard deviation. Y has a 0.5 correlation with X, and a -0.5 correlation with Z. If not provided Y, your algorithm might come up with 1.5X+0.5Z as an approximation - extracting a bit of the signal for Y from the things it does have access to. It's suboptimal, but better than just X+Z. Unfortunately, Y is verboten - we're not allowed to discriminate on it, and this approximation ends up with results that track Y. So instead we train with X, Y and Z as inputs, so the derived model is X+Y+Z - and we can drop Y from that model in production, leading to a model that (while less accurate) shouldn't unfairly track Y.
      - int_19h 7y ago
        > And if those results change, so will the algorithm's outputs! But asking the algorithm to make the change seems to be a bit much. The problem is that the output of those algorithms is used to drive decision making that has the effect of maintaining the status quo, by removing opportunities to change it.
        manfredo 7y ago
        Which loops back around to my original point, which is that the notion that we should alter or influence the algorithm because its output does not match our worldview or politics is not the removal of bias it is the deliberate injection of our personal biases. The whole point of using the algorithm was to make sure personal biases aren't impacting the decision. If we're going to alter the algorithm because we don't like the result, then why are we bothering to use an algorithm in the first place? Just use a human to make the decision. At least in that scenario potential biases have an identifiable source, as opposed to an opaque program that may have been made by engineers that deliberately tuned it to avoid any disparities because they think any disparity in outcome is fundamentally problematic
  - 40acres 7y ago
    This is exactly the problem. An AI classifier without sufficient context is simply a very efficient discrimination machine. We must make sure that AI systems have enough context and protection from human bias before large scale deployment.
gbrown 7y ago
You are correct about the distribution of certain crimes, but you've entirely missed the point.* Problems like violence have multiple causes, but are widely understood to be linked to problems of poverty, inequality, and marginalization. Violence is also self-perpetuating through social networks. When an incautious user of statistical tools fails to investigate the causal story behind such findings, they're going to get the wrong answer. When a policymaker acts on incomplete or misleading findings, they're going to make the problem worse. *Unless you're arguing that it's the fact of being non-white which makes people more violent, in which case I have nothing to say to you.
- kempbellt 7y ago
  > Unless you're arguing that it's the fact of being non-white which makes people more violent This is not how I interpreted OPs comment at all. I just read it as a note that data shows correlation between minority communities and crime. Minority is a flexible, relative term, to my understanding - which, notably in recent years is frequently attributed to "non-white", but over time has been attributed to many different groups of people (caucasian as well). Interpreting it as meaning "non-white" in this context is yet another good example of our predisposed biases and relativistic understandings at play, I suppose. Not to harp on you - just noting the differences in our perceptions.
  - gbrown 7y ago
    I totally understand, and maybe I shouldn't have included that, but I do see phrases like "It's unpopular to talk about" get used as dog whistles all the time in other forums (and in real life). There's a whole subculture dedicated to dressing up racism in pseudo-scientific language (race realism etc.) At the end of the day, there's enough real, vile bigotry out there in our society, I think it's important to be extra clear when discussing topics like this.
    - kempbellt 7y ago
      I'm glad you did. It's a great example of the problem we are discussing. I agree, there is are many negative biases in the real world. More so, I'd argue, on the internet, where people feel safer at a distance to state disagreeable opinions. It's worth noting, especially when we are discussing the nature of how an AI learns.
dmwallin 7y ago
The problem is that these systems are fundamentally unable to distinguish causation from correlation. Which is admittedly a hard prob even for humans, but at least we have some capacity to tease these out. In this case, the increased crime in these communities are not caused by their minority status, but rather from a multitude of other factors with historical and societal origins.
- whatshisface 7y ago
  Cynically, does a bank care why zipcode is correlated with failure to repay loans? Forcing the bank to act as if the probability distributions are different from how they really are sounds like a very awkward way to redistribute wealth. Maybe the right answer would be reparations for whatever society did to everyone in those communities and then total freedom of association for banks and others to give loans as they see fit. Another option would be to give a bad credit subsidy to people in the cases where their low estimated trustworthiness is judged to be due to someone else's error. For example if a factory gives me lead poisoning, my interest rates go up because people with high blood lead content are less likely to keep good credit, but the factory has to pay the difference because they were liable. This reminds me of the argument for the UBI. Instead of having thousands of tiny chairty programs sprinkled all over society, why not let the economy make itself as efficient as possible and then hand out the charity in units of dollars?
  - empath75 7y ago
    Redlining: https://en.wikipedia.org/wiki/Redlining https://en.wikipedia.org/wiki/Redlining
    - deleted 7y ago
      [deleted]
- hr5eaqhera 7y ago
  > unable to distinguish causation from correlation More specifically, the current breed of machine learning is correlation engine, the only strength these networks have is finding correlations in the absence of context or explanation.
empath75 7y ago
There is more crime in poor communities everywhere, and minority communities are more likely to be poor. Moreover, the sorts of crimes that poor people commit are more likely to be prosecuted, and minorities are more likely to be prosecuted for them.

crooked-v 7y ago

> there is quantifiably more crime in some minority communities Sure... and what about the chronic lead exposure issues in many of these areas, the effects of long-term overpolicing of minor crimes on family stability, and the impact of (lack of) generational wealth and education from being used as de facto or literal slaves for generations? The 'minority' part here is a correlative factor, not a causative one.

nostromo 7y ago
An AI classifier doesn't know, or care, why a particular population has higher crime and lower income, it only recognizes and reports on the pattern.
- gbrown 7y ago
  EXACTLY
  - manfredo 7y ago
    But I think you're missing the point. Deliberately altering the model to produce equal results despite unequal patterns in reality is not the elimination of bias, it's introducing bias. Is the goal of the AI model to predict crime rates in a hypothetical world where everyone has equal rates of lead exposure? Or is the goal of the model to predict crime rates in the real world?
    - crooked-v 7y ago
      It depends on what you're using it for and why. If you're concretely distributing police resources, you probably want short-term prediction of actual aggregate crime rates (but also want to consider the risk of overpolicing based on crime rates only recorded higher because of historical overpolicing). On the other hand, if you want to understand anything about the populations involved on some abstract level, merely plugging in predicted crime rates based on historical data won't help at all.
      - manfredo 7y ago
        This comment chain is in reference to law enforcement and the justice system. I would hope that our law enforcement and justice systems are operating on real world data, rather than a hypothetical world's data.
        crooked-v 7y ago
        I would absolutely not want the justice system to use historical statistical information without considering factors like lead exposure and past overpolicing, and even pure boots-on-ground distributions of law enforcement resources should take into account historical over- and under-policing and its effects on the statistics being used to distribute those resources.
        manfredo 7y ago
        Okay, that's your position. Plenty of people do not want an AI that, say, gives parole to someone that is a high risk of re-offending because someone engineered the AI to be more lenient towards people who grew up in areas with greater lead exposure.
        crooked-v 7y ago
        Consider if someone grew up in an area of high lead exposure but was, one way or another, protected from it as a child [1]. Should they be treated as having the same recidivism risk factor as someone who suffered the full effects of lead exposure? If not, how do you separate the effects of lead exposure from other factors to recidivism rates? [1] See the 1999 documentary Blast from the Past.
  - nightski 7y ago
    If you already know what you want the model to say ahead of time and are tweaking it to fit that narrative then there is no point in creating a model in the first place.
- crooked-v 7y ago
  ...which is exactly the kind of "treating culturally- and situationally-contextual results like inherent facts" bias the article is talking about.
  - DuskStar 7y ago
    And if those results change, so will the algorithm's outputs! But asking the algorithm to make the change seems to be a bit much. Honestly, my preferred method of solving this would be to train the algorithm on a data set with all of the forbidden values included along with anything else the creator feels relevant - zip code, income, familial status, favorite sport, education - and then when running in production, against real people, don't give it the restricted information. Yes, you could theoretically extract race, gender and other protected stats from the information the algorithm actually uses in prod - but it has no incentive to, since a less-noisy signal is already provided. For instance, suppose the optimal algorithm for your data set is some linear function of X, Y and Z - let's say X+Y+Z to keep things simple. X,Y and Z are all normally distributed variables, mean of 0 and the same standard deviation. Y has a 0.5 correlation with X, and a -0.5 correlation with Z. If not provided Y, your algorithm might come up with 1.5X+0.5Z as an approximation - extracting a bit of the signal for Y from the things it does have access to. It's suboptimal, but better than just X+Z. Unfortunately, Y is verboten - we're not allowed to discriminate on it, and this approximation ends up with results that track Y. So instead we train with X, Y and Z as inputs, so the derived model is X+Y+Z - and we can drop Y from that model in production, leading to a model that (while less accurate) shouldn't unfairly track Y.
    - int_19h 7y ago
      > And if those results change, so will the algorithm's outputs! But asking the algorithm to make the change seems to be a bit much. The problem is that the output of those algorithms is used to drive decision making that has the effect of maintaining the status quo, by removing opportunities to change it.
      - manfredo 7y ago
        Which loops back around to my original point, which is that the notion that we should alter or influence the algorithm because its output does not match our worldview or politics is not the removal of bias it is the deliberate injection of our personal biases. The whole point of using the algorithm was to make sure personal biases aren't impacting the decision. If we're going to alter the algorithm because we don't like the result, then why are we bothering to use an algorithm in the first place? Just use a human to make the decision. At least in that scenario potential biases have an identifiable source, as opposed to an opaque program that may have been made by engineers that deliberately tuned it to avoid any disparities because they think any disparity in outcome is fundamentally problematic
- 40acres 7y ago
  This is exactly the problem. An AI classifier without sufficient context is simply a very efficient discrimination machine. We must make sure that AI systems have enough context and protection from human bias before large scale deployment.

gbrown 7y ago

You are correct about the distribution of certain crimes, but you've entirely missed the point.* Problems like violence have multiple causes, but are widely understood to be linked to problems of poverty, inequality, and marginalization. Violence is also self-perpetuating through social networks. When an incautious user of statistical tools fails to investigate the causal story behind such findings, they're going to get the wrong answer. When a policymaker acts on incomplete or misleading findings, they're going to make the problem worse. *Unless you're arguing that it's the fact of being non-white which makes people more violent, in which case I have nothing to say to you.

kempbellt 7y ago
> Unless you're arguing that it's the fact of being non-white which makes people more violent This is not how I interpreted OPs comment at all. I just read it as a note that data shows correlation between minority communities and crime. Minority is a flexible, relative term, to my understanding - which, notably in recent years is frequently attributed to "non-white", but over time has been attributed to many different groups of people (caucasian as well). Interpreting it as meaning "non-white" in this context is yet another good example of our predisposed biases and relativistic understandings at play, I suppose. Not to harp on you - just noting the differences in our perceptions.
- gbrown 7y ago
  I totally understand, and maybe I shouldn't have included that, but I do see phrases like "It's unpopular to talk about" get used as dog whistles all the time in other forums (and in real life). There's a whole subculture dedicated to dressing up racism in pseudo-scientific language (race realism etc.) At the end of the day, there's enough real, vile bigotry out there in our society, I think it's important to be extra clear when discussing topics like this.
  - kempbellt 7y ago
    I'm glad you did. It's a great example of the problem we are discussing. I agree, there is are many negative biases in the real world. More so, I'd argue, on the internet, where people feel safer at a distance to state disagreeable opinions. It's worth noting, especially when we are discussing the nature of how an AI learns.

dmwallin 7y ago

The problem is that these systems are fundamentally unable to distinguish causation from correlation. Which is admittedly a hard prob even for humans, but at least we have some capacity to tease these out. In this case, the increased crime in these communities are not caused by their minority status, but rather from a multitude of other factors with historical and societal origins.

whatshisface 7y ago
Cynically, does a bank care why zipcode is correlated with failure to repay loans? Forcing the bank to act as if the probability distributions are different from how they really are sounds like a very awkward way to redistribute wealth. Maybe the right answer would be reparations for whatever society did to everyone in those communities and then total freedom of association for banks and others to give loans as they see fit. Another option would be to give a bad credit subsidy to people in the cases where their low estimated trustworthiness is judged to be due to someone else's error. For example if a factory gives me lead poisoning, my interest rates go up because people with high blood lead content are less likely to keep good credit, but the factory has to pay the difference because they were liable. This reminds me of the argument for the UBI. Instead of having thousands of tiny chairty programs sprinkled all over society, why not let the economy make itself as efficient as possible and then hand out the charity in units of dollars?
- empath75 7y ago
  Redlining: https://en.wikipedia.org/wiki/Redlining https://en.wikipedia.org/wiki/Redlining
  - deleted 7y ago
    [deleted]
hr5eaqhera 7y ago
> unable to distinguish causation from correlation More specifically, the current breed of machine learning is correlation engine, the only strength these networks have is finding correlations in the absence of context or explanation.

empath75 7y ago

There is more crime in poor communities everywhere, and minority communities are more likely to be poor. Moreover, the sorts of crimes that poor people commit are more likely to be prosecuted, and minorities are more likely to be prosecuted for them.