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> That's also NOT what machine learning algorithms do. A machine learning algorithm run on non-representative input could be said to lack experience with the p

> That's also NOT what machine learning algorithms do. A machine learning algorithm run on non-representative input could be said to lack experience with the portion of input that isn't represented in the training set. E.g. I think it's pretty obvious that machine learning algorithms exhibit far more vernacular-bias than purely logical reasoning techniques. > I made this claim given that I explicitly listed two examples of algorithms which are biased. Your parent is pointing out the very real possibility that technically-unbiased algorithms can produce vernacularly-biased outputs. I.e., that statistic's definition of "bias" does not sufficiently capture the notion of bias as it's used in the vernacular. I don't think you've refuted that claim. At the end of the day, your approach toward side-stepping the issue of vernacular bias is intellectually lazy. Instead of tackling the problem head on, you're hiding behind a mathematical object that happens to have the same name as the actual thing under discussion. > I'm beginning to think you are seeking to derail the conversation rather than discussing statistics in good faith. I think your characterization of bias in terms of statistic's technical definition is already skimming the edges of good faith. You know what the reporter means when they say "bias", and that's not the meaning that statisticians use in technical settings.

yummyfajitas 10y ago
I know what the reporter means: "bias" refers to "outcomes the reporter dislikes and thinks he can generate clickbait with". I'm explicitly advocating against this practice and in favor of using clear and precise language instead. At the end of the day, your approach toward side-stepping the issue of vernacular bias is intellectually lazy. Instead of tackling the problem head on, you're hiding behind a mathematical object that happens to have the same name as the actual thing under discussion. I'm not side stepping the issue. I'm advocating in favor of describing the problem with clear language - once that's done we can begin useful discussion on how to address it. In precise terms, what do you think the problem is?
- smallnamespace 10y ago
  Didn't he make that clear? > Your parent is pointing out the very real possibility that _technically-unbiased algorithms can produce vernacularly-biased outputs_. I.e., that statistic's definition of "bias" does not sufficiently capture the notion of bias as it's used in the vernacular.
- throwaway729 10y ago
  > "bias" refers to "outcomes the reporter dislikes and thinks he can generate clickbait with" That's quite a straw man for someone who claims others are trying to derail a conversation. > I'm explicitly advocating against this practice and in favor of using clear and precise language instead. The language you're suggesting masks the problem. It's nice that statisticians came up with a mathematical object that happens to have a politically charged name, but the conflation you're making is not an argument, and doesn't address the underlying (inherently political) problem. > In precise terms, what do you think the problem is? The precise problem is that things like skin color etc. often have strong predictive power. We've recognized this unfortunate fact -- and its societal implications -- and decided to create laws and social norms that regulate (either restrict or sometimes even require) the use of these properties in certain high-impact decisions. Unfortunately, algorithms sometimes use these features to make decisions in settings where a typical human wouldn't (or might even get into legal hot water if they did). Notice that an algorithm can exhibit this behavior without being (statistically) biased. Now, perhaps you think this problem isn't actually a problem. And you can certainly make the argument that any form of vernacular-discrimination should be OK as long as it doesn't exhibit statistical bias. But that's a separate conversation, and you can't defend that position by playing with definitions. And if you're not making that argument, then it should be clear why vernacular-bias differs from statistical-bias.
  - yummyfajitas 10y ago
    You and pessimizer have just demonstrated pretty clearly why Techcrunch's language choices are bad. Pessimizer thinks the article is discussing inaccurate conclusions and that race realism is wrong ( https://news.ycombinator.com/item?id=13158797 https://news.ycombinator.com/item?id=13158797 ). You think race realism is accurate but we should ignore it. You both read the same article but drew totally different conclusions due to it's imprecise language. Many examples in the article don't fit your description of the problem at all (e.g. difficulties in image processing, Tay the trolled chatbot, filter bubbles), so I don't think you are correctly describing what the article means by "bias" either. I can't figure it out either; the best I can come up with is "AI-related things that make the author have negative feelings". I didn't argue in favor of or against your mysterious "vernacular-discrimination" - how could I when I don't even know what it is? The only thing that I argued is that the imprecise language used by Techcrunch is confusing people and that this is bad.

yummyfajitas 10y ago

I know what the reporter means: "bias" refers to "outcomes the reporter dislikes and thinks he can generate clickbait with". I'm explicitly advocating against this practice and in favor of using clear and precise language instead. At the end of the day, your approach toward side-stepping the issue of vernacular bias is intellectually lazy. Instead of tackling the problem head on, you're hiding behind a mathematical object that happens to have the same name as the actual thing under discussion. I'm not side stepping the issue. I'm advocating in favor of describing the problem with clear language - once that's done we can begin useful discussion on how to address it. In precise terms, what do you think the problem is?

smallnamespace 10y ago
Didn't he make that clear? > Your parent is pointing out the very real possibility that _technically-unbiased algorithms can produce vernacularly-biased outputs_. I.e., that statistic's definition of "bias" does not sufficiently capture the notion of bias as it's used in the vernacular.
throwaway729 10y ago
> "bias" refers to "outcomes the reporter dislikes and thinks he can generate clickbait with" That's quite a straw man for someone who claims others are trying to derail a conversation. > I'm explicitly advocating against this practice and in favor of using clear and precise language instead. The language you're suggesting masks the problem. It's nice that statisticians came up with a mathematical object that happens to have a politically charged name, but the conflation you're making is not an argument, and doesn't address the underlying (inherently political) problem. > In precise terms, what do you think the problem is? The precise problem is that things like skin color etc. often have strong predictive power. We've recognized this unfortunate fact -- and its societal implications -- and decided to create laws and social norms that regulate (either restrict or sometimes even require) the use of these properties in certain high-impact decisions. Unfortunately, algorithms sometimes use these features to make decisions in settings where a typical human wouldn't (or might even get into legal hot water if they did). Notice that an algorithm can exhibit this behavior without being (statistically) biased. Now, perhaps you think this problem isn't actually a problem. And you can certainly make the argument that any form of vernacular-discrimination should be OK as long as it doesn't exhibit statistical bias. But that's a separate conversation, and you can't defend that position by playing with definitions. And if you're not making that argument, then it should be clear why vernacular-bias differs from statistical-bias.
- yummyfajitas 10y ago
  You and pessimizer have just demonstrated pretty clearly why Techcrunch's language choices are bad. Pessimizer thinks the article is discussing inaccurate conclusions and that race realism is wrong ( https://news.ycombinator.com/item?id=13158797 https://news.ycombinator.com/item?id=13158797 ). You think race realism is accurate but we should ignore it. You both read the same article but drew totally different conclusions due to it's imprecise language. Many examples in the article don't fit your description of the problem at all (e.g. difficulties in image processing, Tay the trolled chatbot, filter bubbles), so I don't think you are correctly describing what the article means by "bias" either. I can't figure it out either; the best I can come up with is "AI-related things that make the author have negative feelings". I didn't argue in favor of or against your mysterious "vernacular-discrimination" - how could I when I don't even know what it is? The only thing that I argued is that the imprecise language used by Techcrunch is confusing people and that this is bad.