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If this were a conversation about a statistics paper or textbook, or a particular ML algorithm, you might have a point. But we're not discussing statistics. We

If this were a conversation about a statistics paper or textbook, or a particular ML algorithm, you might have a point. But we're not discussing statistics. We're discussing the social impact of ML algorithms. > Why risk such confusion when we can just use two different words for two different concepts? The problem with this is that we'll always have that confusion because most people aren't statistics experts. So it's up to the domain experts to know when one meaning or the other is intended. That's true in every domain. > However, you are also wrong about the vernacular sense, at least if the dictionary is to be believed Dictionary definitions don't fully capture the vernacular meaning of words -- especially politically charged words such as "bias". > This are just informal versions of the statistical term. But when real people use the term bias in real conversations, they aren't typically approximating the statistical meaning. Rather, they are typically referring to some a priori ethical limitations on decision making. For example, most people would say that taking skin color into account when approving a housing loan is a form of bias. But they don't mean that in any particularly technical way. There absolutely is moral and political content in the term "bias". For example, when equal lending laws were passed, an unbiased algorithm definitely would've considered race when choosing the outcome of a loan application. African Americans were systematically discriminated against in employment decisions, and often the first to be let go when hard times hit. So a completely unbiased loan officer could come to the statistically valid conclusion that people of color were far more likely to default. None-the-less, a vase majority of people today would agree that the loan officer was exhibiting racial bias. And they're not coming to that conclusion due to innumeracy or some vacuous personal preference. They're perceiving a vast network of causal relationships between decisions that reinforce a systematic bias, even when many of those decisions aren't locally statistically biased > I don't think we should use the term "warm" to reference "feelings in my heart". And I agree, but I agree because non-experts know and understand the difference. And because the term "global warming" with the alternative meaning isn't already in wide-spread vernacular use.

AnthonyMouse 10y ago
> But when real people use the term bias in real conversations, they aren't typically approximating the statistical meaning. Rather, they are typically referring to some a priori ethical limitations on decision making. What many common people mean by bias is approximately the statistical meaning. For example, if the algorithm accurately predicts the number of white defendants who fail to appear in court but overestimates the number of black defendants who fail to appear, people in common usage will call that bias. Which it is, in the statistical sense. But if a higher percentage of black defendants actually fail to appear than white defendants, and the algorithm accurately predicts that outcome, that isn't bias in the statistical sense. And the people who try to conflate those things are doing this: http://slatestarcodex.com/2014/11/03/all-in-all-another-brick-in-the-motte/ http://slatestarcodex.com/2014/11/03/all-in-all-another-bric... The second thing is not only not bias, it's not morally objectionable. Statistical bias is the thing which is morally objectionable. Calling something else "bias" is not just inaccurate, it leads directly to a false moral conclusion.
- throwaway729 10y ago
  > What many common people mean by bias is approximately the statistical meaning. And many people don't. The difference is that I'm not asserting I have the one true definition, but rather recognizing that "bias" is a term with inherent political and moral content. And that pretending that this isn't (or shouldn't be) the case just because statisticians use the term in a technical setting is extraordinarily intellectually lazy. Most people are not statisticians. No serious mathematician has ever asserted that the axioms of Real numbers settle all epistemological debates. Like-wise, I see no reason why a statistical term of art should be used to clear up the various denotations and connotations surrounding "bias" and "discrimination". > that isn't bias in the statistical sense. That depends on what the goal of the algorithm is. If the goal is to accurately predict whether people will appear, then it's working. But if the goal is to accurately predict when people will choose not to show up, then there's certainly a chance that it's not working. For example, in the case where there's some significant correlation between race and access to transportation infrastructure. (Which isn't so much the case today, but certainly was 60 or so years ago.) The distinction between choice and circumstance isn't immaterial -- the latter is not a flight risk even if they gum up the legal system via delays. So if the judge is interpreting "90% won't show" as "90% flight risk" in the above hypothetical world, then there's a huge problem. You could argue that this is the judge's problem, and I won't necessarily disagree. But UI bugs are still bugs, and this particular form of UI bug is pretty specific to ML systems. > And the people who try to conflate those things are doing this Or, perhaps they're far better at mathematical modeling of complex systems than you give them credit for; e.g., by seeing ways in which locally statistically unbiased decisions can magnify the impact of bias that exists in other parts of a system. We will never have a complete mathematical model of the world and the history of human civilization, so I don't see any reason why we should insist that all bias needs to be what a statistician calls bias in his workplace. > The second thing is not only not bias, it's not morally objectionable This is a separate discussion I don't want to have in this thread, because we're already barely on-topic. But suffice it to say that conflating technical and non-technical definitions is not a reasonable defense of this position.
  - AnthonyMouse 10y ago
deleted 10y ago
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AnthonyMouse 10y ago

> But when real people use the term bias in real conversations, they aren't typically approximating the statistical meaning. Rather, they are typically referring to some a priori ethical limitations on decision making. What many common people mean by bias is approximately the statistical meaning. For example, if the algorithm accurately predicts the number of white defendants who fail to appear in court but overestimates the number of black defendants who fail to appear, people in common usage will call that bias. Which it is, in the statistical sense. But if a higher percentage of black defendants actually fail to appear than white defendants, and the algorithm accurately predicts that outcome, that isn't bias in the statistical sense. And the people who try to conflate those things are doing this: http://slatestarcodex.com/2014/11/03/all-in-all-another-brick-in-the-motte/ http://slatestarcodex.com/2014/11/03/all-in-all-another-bric... The second thing is not only not bias, it's not morally objectionable. Statistical bias is the thing which is morally objectionable. Calling something else "bias" is not just inaccurate, it leads directly to a false moral conclusion.

throwaway729 10y ago
> What many common people mean by bias is approximately the statistical meaning. And many people don't. The difference is that I'm not asserting I have the one true definition, but rather recognizing that "bias" is a term with inherent political and moral content. And that pretending that this isn't (or shouldn't be) the case just because statisticians use the term in a technical setting is extraordinarily intellectually lazy. Most people are not statisticians. No serious mathematician has ever asserted that the axioms of Real numbers settle all epistemological debates. Like-wise, I see no reason why a statistical term of art should be used to clear up the various denotations and connotations surrounding "bias" and "discrimination". > that isn't bias in the statistical sense. That depends on what the goal of the algorithm is. If the goal is to accurately predict whether people will appear, then it's working. But if the goal is to accurately predict when people will choose not to show up, then there's certainly a chance that it's not working. For example, in the case where there's some significant correlation between race and access to transportation infrastructure. (Which isn't so much the case today, but certainly was 60 or so years ago.) The distinction between choice and circumstance isn't immaterial -- the latter is not a flight risk even if they gum up the legal system via delays. So if the judge is interpreting "90% won't show" as "90% flight risk" in the above hypothetical world, then there's a huge problem. You could argue that this is the judge's problem, and I won't necessarily disagree. But UI bugs are still bugs, and this particular form of UI bug is pretty specific to ML systems. > And the people who try to conflate those things are doing this Or, perhaps they're far better at mathematical modeling of complex systems than you give them credit for; e.g., by seeing ways in which locally statistically unbiased decisions can magnify the impact of bias that exists in other parts of a system. We will never have a complete mathematical model of the world and the history of human civilization, so I don't see any reason why we should insist that all bias needs to be what a statistician calls bias in his workplace. > The second thing is not only not bias, it's not morally objectionable This is a separate discussion I don't want to have in this thread, because we're already barely on-topic. But suffice it to say that conflating technical and non-technical definitions is not a reasonable defense of this position.
- AnthonyMouse 10y ago

deleted 10y ago

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