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> 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

> 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
> 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. Your assertion seems to be that not using the same word to describe separate and distinguishable things in a context where the distinction matters is intellectually lazy. > 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 goal is to predict whether people will appear. The bail-bondsman has to know whether to issue the bail-bond. If your issue is transportation infrastructure then the only way to solve it is by fixing the transportation infrastructure, not by bankrupting the bail-bondsman. > This is a separate discussion I don't want to have. > But suffice it to say that conflating technical and non-technical definitions is not a reasonable defense of this position. That's not the point -- the problem is that conflating technical and non-technical definitions is a way to avoid having that discussion. Effectively everyone agrees that statistical bias is wrong. If you're on the wrong end of it you're being treated unfairly. If you can convince someone that something is this "bias" when everyone agrees bias is wrong then you've convinced them that the thing is wrong. But if all you've done is broaden the scope of what bias means to encompass something that not everyone agrees is wrong then you've accomplished nothing of virtue. And more than likely misled some number of people who haven't managed to piece together that the "bias" they understand to be wrong and the "bias" you understand to be present are non-overlapping.
- yummyfajitas 10y ago
  Effectively everyone agrees that statistical bias is wrong. If you're on the wrong end of it you're being treated unfairly. That's not quite true. An unbiased credit scoring algorithm would penalize blacks, subtracting 50 pts or so from their FICO score. Is it fair to reject a loan because of the ethnic background of the applicant? That seems unfair to me. Then again forcing a lender to give out loans that he knows will default also seems unfair, to say nothing of the higher interest rates that Asians must pay in order to subsidize deadbeat blacks. I don't quite know what "fair" means here. What I do know is that the term "bias" is used to mislead; to hide the fact that whatever choice we make, there is a cost to be paid.

AnthonyMouse 10y ago

> 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. Your assertion seems to be that not using the same word to describe separate and distinguishable things in a context where the distinction matters is intellectually lazy. > 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 goal is to predict whether people will appear. The bail-bondsman has to know whether to issue the bail-bond. If your issue is transportation infrastructure then the only way to solve it is by fixing the transportation infrastructure, not by bankrupting the bail-bondsman. > This is a separate discussion I don't want to have. > But suffice it to say that conflating technical and non-technical definitions is not a reasonable defense of this position. That's not the point -- the problem is that conflating technical and non-technical definitions is a way to avoid having that discussion. Effectively everyone agrees that statistical bias is wrong. If you're on the wrong end of it you're being treated unfairly. If you can convince someone that something is this "bias" when everyone agrees bias is wrong then you've convinced them that the thing is wrong. But if all you've done is broaden the scope of what bias means to encompass something that not everyone agrees is wrong then you've accomplished nothing of virtue. And more than likely misled some number of people who haven't managed to piece together that the "bias" they understand to be wrong and the "bias" you understand to be present are non-overlapping.

yummyfajitas 10y ago
Effectively everyone agrees that statistical bias is wrong. If you're on the wrong end of it you're being treated unfairly. That's not quite true. An unbiased credit scoring algorithm would penalize blacks, subtracting 50 pts or so from their FICO score. Is it fair to reject a loan because of the ethnic background of the applicant? That seems unfair to me. Then again forcing a lender to give out loans that he knows will default also seems unfair, to say nothing of the higher interest rates that Asians must pay in order to subsidize deadbeat blacks. I don't quite know what "fair" means here. What I do know is that the term "bias" is used to mislead; to hide the fact that whatever choice we make, there is a cost to be paid.