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> The next step is establishing just how much of the male-female difference is explained by biology. I haven't studied that part of it. But the "null hypothesis

by gbrown 7y ago

> The next step is establishing just how much of the male-female difference is explained by biology. I haven't studied that part of it. But the "null hypothesis", that all gender differences in behavior are due solely to social conditioning, is wrong. This is interesting and important science, and you're right that clearly there are some gender differences... BUT 1. Given the history of (pseudo)science in justifying and worsening disparities between groups, incredible caution is needed - especially when making policy. 2. The existence of biological phenomena in no way discounts the strong social cues and pressures which shape young humans. 3. The existence of detectable, biologically originating average differences between genders in no way justifies the use of algorithms in such a way that individuals are punished for their group membership. On HN, these threads always seem to go straight to "BUT THERE REALLY ARE DIFFERENCES!!", which in my mind is completely missing the point.

waterhouse 7y ago
The flip side is going straight from "The fraction of women in X group is much less than 50%" to "Therefore someone is discriminating, creating a hostile environment, socially conditioning people, and/or otherwise behaving badly—and if everyone involved appears to be behaving well, then it must be due to either someone unconsciously doing these things, or someone hiding their bad behavior, which we will proceed to expose by calling out a scapegoat, applying the worst interpretation to all the ambiguous things they do, and attempting to ruin their careers." I would say that incredible caution is needed on that side too. In terms of algorithms: What do you want your algorithm to produce? Gender parity? A distribution that matches what would be reality if biology were an influence but social conditioning weren't? Maybe what would be reality if biology were an influence, and people were encouraged to follow their passion, whatever it is, but scrupulously avoided recommending any particular activities to particular children until after learning what their passion was? ... Or do you want it to analyze reality as it is and produce decisions that are useful in this reality? Ideally, the algorithm would be fed enough data about the people it's judging that it could make a properly fair decision. If all the algorithm gets is "Male, 22, minored in CS; female, 22, minored in CS", then the only thing it has to go on is the sex difference, which is probably a significant one, and then we can argue about what it should do. However, if it gets "Female, 22, minored in CS, majored in math and was top of her class, has spent 400 hours on personal programming projects / Project Euler / Topcoder / chatting about data structures with her friends, has been using the Unix command line on a regular basis since age 12", that looks like a much stronger candidate—and, importantly for this discussion, a male with those same qualifications would probably be an equally good candidate. I think that, if you have the same level of intelligence (perhaps specifically the math/symbol-manipulation areas), the same level of enthusiasm, and spend the same number of hours on the same activities, then you should get roughly the same results no matter what your sex is; sex is only useful to guess at the enthusiasm and hours spent when those variables are missing from the inputs. That's the solution as far as I'm concerned: More input data so the algorithm can be fair. It'll consider all the people who've spent 1000 hours studying and practicing programming and achieved the same level, and it'll judge them the same, and that is fair, even if it happens that 80% of those people are men. Unfair would be if the algorithm only gets the superficial data and has to guess, based on group averages, on everything important, and penalizes the truly capable women because it's unable to distinguish them from the average woman.
- gbrown 7y ago
  1. I feel like your flip side is a straw man. 2. Your description of algorithms and how they can/should behave is completely beside the point. Thats the problem with vague marketing language like "AI". The article isn't about artificial generalized intelligence, it's about machine learning algorithms used widely today. These are problems now, not hypothetical discussions about how we want some idealized "AI" to behave.

by gbrown 7y ago

waterhouse 7y ago

The flip side is going straight from "The fraction of women in X group is much less than 50%" to "Therefore someone is discriminating, creating a hostile environment, socially conditioning people, and/or otherwise behaving badly—and if everyone involved appears to be behaving well, then it must be due to either someone unconsciously doing these things, or someone hiding their bad behavior, which we will proceed to expose by calling out a scapegoat, applying the worst interpretation to all the ambiguous things they do, and attempting to ruin their careers." I would say that incredible caution is needed on that side too. In terms of algorithms: What do you want your algorithm to produce? Gender parity? A distribution that matches what would be reality if biology were an influence but social conditioning weren't? Maybe what would be reality if biology were an influence, and people were encouraged to follow their passion, whatever it is, but scrupulously avoided recommending any particular activities to particular children until after learning what their passion was? ... Or do you want it to analyze reality as it is and produce decisions that are useful in this reality? Ideally, the algorithm would be fed enough data about the people it's judging that it could make a properly fair decision. If all the algorithm gets is "Male, 22, minored in CS; female, 22, minored in CS", then the only thing it has to go on is the sex difference, which is probably a significant one, and then we can argue about what it should do. However, if it gets "Female, 22, minored in CS, majored in math and was top of her class, has spent 400 hours on personal programming projects / Project Euler / Topcoder / chatting about data structures with her friends, has been using the Unix command line on a regular basis since age 12", that looks like a much stronger candidate—and, importantly for this discussion, a male with those same qualifications would probably be an equally good candidate. I think that, if you have the same level of intelligence (perhaps specifically the math/symbol-manipulation areas), the same level of enthusiasm, and spend the same number of hours on the same activities, then you should get roughly the same results no matter what your sex is; sex is only useful to guess at the enthusiasm and hours spent when those variables are missing from the inputs. That's the solution as far as I'm concerned: More input data so the algorithm can be fair. It'll consider all the people who've spent 1000 hours studying and practicing programming and achieved the same level, and it'll judge them the same, and that is fair, even if it happens that 80% of those people are men. Unfair would be if the algorithm only gets the superficial data and has to guess, based on group averages, on everything important, and penalizes the truly capable women because it's unable to distinguish them from the average woman.

gbrown 7y ago
1. I feel like your flip side is a straw man. 2. Your description of algorithms and how they can/should behave is completely beside the point. Thats the problem with vague marketing language like "AI". The article isn't about artificial generalized intelligence, it's about machine learning algorithms used widely today. These are problems now, not hypothetical discussions about how we want some idealized "AI" to behave.