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> The lower certainty would in turn lead to lower rankings for women even without any bias in the data. I don't think that's true. "No bias" means that gender

by tomp 8y ago

> The lower certainty would in turn lead to lower rankings for women even without any bias in the data. I don't think that's true. "No bias" means that gender is irrelevant (i.e. its correlation with outcome is 0%). Therefore the system shouldn't even take it into account - it would evaluate both men and women just by other criteria (experience, technical skills, etc), and it would have equal amounts of data for both (because it wouldn't even see them as different). You need bias to even separate the dataset into distinct categories.

theptip 8y ago
> "No bias" means that gender is irrelevant False. If we're talking about the technical statistical definition, bias means systematic deviation from the underlying truth in the data -- see this article by Chris Stucchio with some images for clarification: https://jacobitemag.com/2017/08/29/a-i-bias-doesnt-mean-what-journalists-want-you-to-think-it-means/ https://jacobitemag.com/2017/08/29/a-i-bias-doesnt-mean-what... "In statistics, a “bias” is defined as a statistical predictor which makes errors that all have the same direction. A separate term — “variance” — is used to describe errors without any particular direction. It’s important to distinguish bias (making errors with a common direction) from variance which is simply inaccuracy with no particular direction."
- deleted 8y ago
  [deleted]
- tomp 8y ago
  I think the comments I replied to mean bias as in “sexist bias”.
  - grandmczeb 8y ago
    Bias as in racism, sexism, etc, has multiple definitions, some of which are mutually exclusive.
  - theptip 8y ago
    Well, it was clear that _you_ think so. My point was that you should consider the meaning of the word under which the post you're replying to is correct, especially given that the author was claiming specific domain experience.
    - tomp 8y ago
      The original was: > The lower certainty would in turn lead to lower rankings for women even without any bias in the data. your post said: > If we're talking about the technical statistical definition, bias means systematic deviation from the underlying truth in the data So I think my interpretation is correct, even though it's not "the technically statistically correct usage". You were referring to the bias of the algorithm (i.e. the mean divergence from the mean in the data), whereas we were referring to the "hiring bias" evident in the data. In fact, your "bias" was mentioned as "lower rankings for women" - i.e. "the algorithm would have (statistical) bias even without (sexist) bias in the data" and I was replying that I think that's false.
- chiefalchemist 8y ago
  Question: So technically, the AI is not bias against women per se, but a set of characteristics / properties, that are more common among women. I'm not trying to split hairs (or argue), as much as further clarify the difference between (the common definition of) human bias and that of statistical bias.
  - zaarn 8y ago
    Correct. Computers are very bad at actually discriminating against people, they will pick up a possible bias in a statistical dataset (ie, <protected class> uses certain sentence structure and is statistically less likely to get or keep the job). Sometimes computers also pick up on statistical truths that we don't like, ie, you assign a ML to classify how likely someone is to pay back their loan and it picks up on poor people and bad neighborhoods, disproportionately affecting people of color or low income households. In theory there is nothing wrong with the data, after all, these are the people who are least likely to pay back a loan, but our moral framework usually classifies this as bad and discriminatory. Machine Learning (AI) doesn't have moral frameworks and doesn't know what the truth is. The answers it can give us may not be answers we like or want or should have. on a side note; human bias is usually not that different since the brain can be simplified as a bayesian filter; there are predictions on the present based on past experience, reevaluation of past experience based on current experience and prediction of future experience based on past and current experience. It's a simplification but usually most human bias is based on one of these, either explicitly social (bad experience with certain classes of people) or implicitly (tribalism).
    - theptip 8y ago
      > the brain can be simplified as a bayesian filter I agree with everything else in your post, but just wanted to note that while this is true to some extent, the brain is much less rational than a pure Bayesian inference system; there are a lot of baked in heuristics designed to short-circuit the collection of data that would be required to make high-quality Bayesian inferences. This is why excessive stereotyping and tribalism are a fundamental human trait; a pure Bayesian system wouldn't jump to conclusions as quickly as humans do, nor would it refuse to change its mind from those hastily-formed opinions.

by tomp 8y ago

theptip 8y ago

> "No bias" means that gender is irrelevant False. If we're talking about the technical statistical definition, bias means systematic deviation from the underlying truth in the data -- see this article by Chris Stucchio with some images for clarification: https://jacobitemag.com/2017/08/29/a-i-bias-doesnt-mean-what-journalists-want-you-to-think-it-means/ https://jacobitemag.com/2017/08/29/a-i-bias-doesnt-mean-what... "In statistics, a “bias” is defined as a statistical predictor which makes errors that all have the same direction. A separate term — “variance” — is used to describe errors without any particular direction. It’s important to distinguish bias (making errors with a common direction) from variance which is simply inaccuracy with no particular direction."

deleted 8y ago
[deleted]
tomp 8y ago
I think the comments I replied to mean bias as in “sexist bias”.
- grandmczeb 8y ago
  Bias as in racism, sexism, etc, has multiple definitions, some of which are mutually exclusive.
- theptip 8y ago
  Well, it was clear that _you_ think so. My point was that you should consider the meaning of the word under which the post you're replying to is correct, especially given that the author was claiming specific domain experience.
  - tomp 8y ago
    The original was: > The lower certainty would in turn lead to lower rankings for women even without any bias in the data. your post said: > If we're talking about the technical statistical definition, bias means systematic deviation from the underlying truth in the data So I think my interpretation is correct, even though it's not "the technically statistically correct usage". You were referring to the bias of the algorithm (i.e. the mean divergence from the mean in the data), whereas we were referring to the "hiring bias" evident in the data. In fact, your "bias" was mentioned as "lower rankings for women" - i.e. "the algorithm would have (statistical) bias even without (sexist) bias in the data" and I was replying that I think that's false.
chiefalchemist 8y ago
Question: So technically, the AI is not bias against women per se, but a set of characteristics / properties, that are more common among women. I'm not trying to split hairs (or argue), as much as further clarify the difference between (the common definition of) human bias and that of statistical bias.
- zaarn 8y ago
  Correct. Computers are very bad at actually discriminating against people, they will pick up a possible bias in a statistical dataset (ie, <protected class> uses certain sentence structure and is statistically less likely to get or keep the job). Sometimes computers also pick up on statistical truths that we don't like, ie, you assign a ML to classify how likely someone is to pay back their loan and it picks up on poor people and bad neighborhoods, disproportionately affecting people of color or low income households. In theory there is nothing wrong with the data, after all, these are the people who are least likely to pay back a loan, but our moral framework usually classifies this as bad and discriminatory. Machine Learning (AI) doesn't have moral frameworks and doesn't know what the truth is. The answers it can give us may not be answers we like or want or should have. on a side note; human bias is usually not that different since the brain can be simplified as a bayesian filter; there are predictions on the present based on past experience, reevaluation of past experience based on current experience and prediction of future experience based on past and current experience. It's a simplification but usually most human bias is based on one of these, either explicitly social (bad experience with certain classes of people) or implicitly (tribalism).
  - theptip 8y ago
    > the brain can be simplified as a bayesian filter I agree with everything else in your post, but just wanted to note that while this is true to some extent, the brain is much less rational than a pure Bayesian inference system; there are a lot of baked in heuristics designed to short-circuit the collection of data that would be required to make high-quality Bayesian inferences. This is why excessive stereotyping and tribalism are a fundamental human trait; a pure Bayesian system wouldn't jump to conclusions as quickly as humans do, nor would it refuse to change its mind from those hastily-formed opinions.