3 ms·

I always thought it was the opposite. That we want AI to have no biases, but we have trouble avoiding it. Choosing data and cleaning is where we insert our bias

I always thought it was the opposite. That we want AI to have no biases, but we have trouble avoiding it. Choosing data and cleaning is where we insert our bias.

deleted 4y ago
[deleted]
fallingknife 4y ago
What people want is for the models to show no differences between certain groups of people. That is the standard they use for "no bias." But that itself is bias, so really they just want the model output to conform to their bias that there isn't any difference.
- p0pcult 4y ago
  In fact, the article cites the kind of bias that warrants eliminating. One example is the misidentification of people with dark skin by facial recognition technology, as this has led to the arrest or incarceration of innocent people. Not, as you completely fabricate, "show no differences between certain groups of people." You, however, are showing your bias.
- diputsmonro 4y ago
  So what you're really saying is, it's perfectly okay for AI to have negative preconceptions and concrete impacts to certain groups of people, as long as it agrees with you on which groups are bad. How would you feel about AI models that classify all modern white people as racists based on overwhelming historical data about slavery, the KKK, etc.? Hey man, data doesn't lie.
  - zmgsabst 4y ago
    Isn’t the media doing exactly that? They fixate on white racists while ignoring (eg) the Christmas parade attack — leading to society developing a bias in their beliefs about racism. I always take these criticism of AI as confession-through-projection of the misdeeds people have engaged in already.
    - gcanyon 4y ago
      I'm gonna need a cite and some statistical analysis to back up your "media is biased" claim. One data point on an event that I easily found reporting on isn't going to cut it.
      - zmgsabst 4y ago
        Will FBI crime statistics do? > In 2019, race was reported for 6,406 known hate crime offenders. Of these offenders: > 52.5 percent were White. > 23.9 percent were Black or African American. https://ucr.fbi.gov/hate-crime/2019/topic-pages/offenders https://ucr.fbi.gov/hate-crime/2019/topic-pages/offenders White people don’t commit disproportionately many hate crimes — black people do. According to the FBI.
        p0pcult 4y ago
        You failed to show evidence that the media is claiming that white people disproportionately commit hate crime.
        gcanyon 4y ago
        Thanks, your point exactly. My subjective and N=1 opinion is that whites are dramatically under-reported as criminals in the media.
    - diputsmonro 4y ago
      Your deflection about the media is entirely unrelated to AI, but it only backs up my argument. If giant media corporations are biased, why can't AI researchers be? Every institution has biases, and blindly trusting a dataset from e.g. a police department is just as bad as blindly trusting data from CNN or Fox News. The collection, aggregation, and sharing of data is a biased operation. If a political or for-profit entity gives you data, it's going to be data that supports their agenda. It takes active, conscious effort to combat that bias, in the same way that it takes active, conscious effort to not just get all your news from one source. The concern (which has been demonstrated) is that that isn't being done
- shadowgovt 4y ago
  Correct. As a tool serving humans, we want the tool to reflect the decisions humans should make given the same broad swath of data. "Unbiased machine learning models" is basically a nonsense idea; a machine learning engine is a discrimination / classification tool, and its entire point is to become biased based on inputs so that on future inputs, its outputs tilt over in a desired fashion instead of just being uncorrelated noise. Making those biases have the desired shape is the art and science of the process.
  - p0pcult 4y ago
    You are conflating the statistical meaning of bias with a social meaning of bias. A statistically unbiased model would be (for example) in which it has the same false positive rate at identifying humans from image data, for varying races, or a model that is equally likely to underpredict as overpredict tomorrow's gas price. These are not nonsense ideas, in fact, they are often good ideas. There are cases, however, where we may want a model to be statistically biased, for example, if the detriment of underpredicting tomorrow's gas price is worse than that of overpredicting it. If the gas price is lower than I expected, maybe it's not so bad that I didn't buy it today. We (well, some of us) also don't want socially/politically biased models. Examples of those are very devious: redlining, for example, prevented black Americans from equal access to housing, historically, and has caused multiple generations of wealth disparity as a result. You are using really loose and sloppy language in your talking about ML; you should be using "trained" and not "biased" because bias has a technical meaning that seems to evade you.
    - shadowgovt 4y ago
      I'm using biased as it is colloquially used and pointing out that when people ask for "unbiased machine learning" they generally need to be more specific, because the whole point of machine learning is to give better answers than random noise.
p0pcult 4y ago
You're right; GP is just trying to make an unwarranted political point.

I always thought it was the opposite. That we want AI to have no biases, but we have trouble avoiding it. Choosing data and cleaning is where we insert our bias.

deleted 4y ago

[deleted]

fallingknife 4y ago

What people want is for the models to show no differences between certain groups of people. That is the standard they use for "no bias." But that itself is bias, so really they just want the model output to conform to their bias that there isn't any difference.

p0pcult 4y ago
In fact, the article cites the kind of bias that warrants eliminating. One example is the misidentification of people with dark skin by facial recognition technology, as this has led to the arrest or incarceration of innocent people. Not, as you completely fabricate, "show no differences between certain groups of people." You, however, are showing your bias.
diputsmonro 4y ago
So what you're really saying is, it's perfectly okay for AI to have negative preconceptions and concrete impacts to certain groups of people, as long as it agrees with you on which groups are bad. How would you feel about AI models that classify all modern white people as racists based on overwhelming historical data about slavery, the KKK, etc.? Hey man, data doesn't lie.
- zmgsabst 4y ago
  Isn’t the media doing exactly that? They fixate on white racists while ignoring (eg) the Christmas parade attack — leading to society developing a bias in their beliefs about racism. I always take these criticism of AI as confession-through-projection of the misdeeds people have engaged in already.
  - gcanyon 4y ago
    I'm gonna need a cite and some statistical analysis to back up your "media is biased" claim. One data point on an event that I easily found reporting on isn't going to cut it.
    - zmgsabst 4y ago
      Will FBI crime statistics do? > In 2019, race was reported for 6,406 known hate crime offenders. Of these offenders: > 52.5 percent were White. > 23.9 percent were Black or African American. https://ucr.fbi.gov/hate-crime/2019/topic-pages/offenders https://ucr.fbi.gov/hate-crime/2019/topic-pages/offenders White people don’t commit disproportionately many hate crimes — black people do. According to the FBI.
      - p0pcult 4y ago
        You failed to show evidence that the media is claiming that white people disproportionately commit hate crime.
        gcanyon 4y ago
        Thanks, your point exactly. My subjective and N=1 opinion is that whites are dramatically under-reported as criminals in the media.
  - diputsmonro 4y ago
    Your deflection about the media is entirely unrelated to AI, but it only backs up my argument. If giant media corporations are biased, why can't AI researchers be? Every institution has biases, and blindly trusting a dataset from e.g. a police department is just as bad as blindly trusting data from CNN or Fox News. The collection, aggregation, and sharing of data is a biased operation. If a political or for-profit entity gives you data, it's going to be data that supports their agenda. It takes active, conscious effort to combat that bias, in the same way that it takes active, conscious effort to not just get all your news from one source. The concern (which has been demonstrated) is that that isn't being done
shadowgovt 4y ago
Correct. As a tool serving humans, we want the tool to reflect the decisions humans should make given the same broad swath of data. "Unbiased machine learning models" is basically a nonsense idea; a machine learning engine is a discrimination / classification tool, and its entire point is to become biased based on inputs so that on future inputs, its outputs tilt over in a desired fashion instead of just being uncorrelated noise. Making those biases have the desired shape is the art and science of the process.
- p0pcult 4y ago
  You are conflating the statistical meaning of bias with a social meaning of bias. A statistically unbiased model would be (for example) in which it has the same false positive rate at identifying humans from image data, for varying races, or a model that is equally likely to underpredict as overpredict tomorrow's gas price. These are not nonsense ideas, in fact, they are often good ideas. There are cases, however, where we may want a model to be statistically biased, for example, if the detriment of underpredicting tomorrow's gas price is worse than that of overpredicting it. If the gas price is lower than I expected, maybe it's not so bad that I didn't buy it today. We (well, some of us) also don't want socially/politically biased models. Examples of those are very devious: redlining, for example, prevented black Americans from equal access to housing, historically, and has caused multiple generations of wealth disparity as a result. You are using really loose and sloppy language in your talking about ML; you should be using "trained" and not "biased" because bias has a technical meaning that seems to evade you.
  - shadowgovt 4y ago
    I'm using biased as it is colloquially used and pointing out that when people ask for "unbiased machine learning" they generally need to be more specific, because the whole point of machine learning is to give better answers than random noise.

p0pcult 4y ago

You're right; GP is just trying to make an unwarranted political point.