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What's wrong with encoding the status quo? Why do we have to encode progressive extremism into computer models? Being progressive for the sake of "going forwar

by ihsw 10y ago

What's wrong with encoding the status quo? Why do we have to encode progressive extremism into computer models? Being progressive for the sake of "going forward" sounds like misguided idealism. What do we do when progressive computer models lead us down a harmful path, do we simply play the typical liberal blame game and point fingers everywhere else while digging our head into sand?

wehere 10y ago
jesus christ
tptacek 10y ago
I'm not sure you're following my point. Conservatives should be no happier than liberals about the prospect of a deeply imperfect status quo being baked into future decisionmaking, because there's much about the status quo conservatives don't like either. This isn't even remotely far-fetched. If you're a rural working class person, it's not at all hard to see how machine learning algorithms could adversely affect decisions about your insurance rates, loan acceptances, sentencing, the frequency with which your homes and businesses are inspected, and so on. The idea that "discrimination" is somehow a progressive bugbear is --- well, I don't know how better to say this --- deeply stupid. Swirling eyes-within-eyes sticking out from blobs of fur inverted machine learning stupid.
- ihsw 10y ago
  I do understand the point you're making -- my concern centers around deeply flawed models being used despite their shortcomings because their inherently progressive slant is sacrosanct. They would instead be held up and fingers pointed elsewhere, either due to lack of data or incorrect usage of the resulting advice derived from existing bodies of data (or something more sinister, eg: sabotage by opposition parties). What I'm trying to say is correcting discrimination under the guise that it's unfair to certain sections of society is chasing your own tail out of boredom -- what's the point? To improve society, or improve usefulness of machine learning-derived data to make radical (but necessary) decisions? Do we maintain our objectivity and move away from "improving society" when it's found to be counter-productive, or do we keep going forward with improving society and hope things get better? Trying to "break out of the status quo" to produce more a accurate representation of the world sounds great, I'm totally all for removing human biases, but my concern is that it'll be handled badly to mistakenly attempt to benefit certain classes of society (eg: repeat of subprime mortgage crises from 07-09 but much more difficult to diagnose).
  - tptacek 10y ago
    I'm not sure you understand my point, because you keep talking about "progressive slant", and I keep trying to explain to you that ML is going to be just as hard --- in fact, probably far harsher --- on "red state" voters. Think of it in terms of survival bias. ML models that discriminate against the well-educated urban workers that design and deploy them are not going to make it to production if they obviously discriminate against their own designers. Those developers and "data scientists" are not going to notice if those same models happen to deny college admission to kids who took a year off after graduating to work at a factory to help pay for their siblings room and board.
    - ihsw 10y ago
      That is actually a good analogy. Thank you for that.

by ihsw 10y ago

wehere 10y ago

jesus christ

tptacek 10y ago

I'm not sure you're following my point. Conservatives should be no happier than liberals about the prospect of a deeply imperfect status quo being baked into future decisionmaking, because there's much about the status quo conservatives don't like either. This isn't even remotely far-fetched. If you're a rural working class person, it's not at all hard to see how machine learning algorithms could adversely affect decisions about your insurance rates, loan acceptances, sentencing, the frequency with which your homes and businesses are inspected, and so on. The idea that "discrimination" is somehow a progressive bugbear is --- well, I don't know how better to say this --- deeply stupid. Swirling eyes-within-eyes sticking out from blobs of fur inverted machine learning stupid.

ihsw 10y ago
I do understand the point you're making -- my concern centers around deeply flawed models being used despite their shortcomings because their inherently progressive slant is sacrosanct. They would instead be held up and fingers pointed elsewhere, either due to lack of data or incorrect usage of the resulting advice derived from existing bodies of data (or something more sinister, eg: sabotage by opposition parties). What I'm trying to say is correcting discrimination under the guise that it's unfair to certain sections of society is chasing your own tail out of boredom -- what's the point? To improve society, or improve usefulness of machine learning-derived data to make radical (but necessary) decisions? Do we maintain our objectivity and move away from "improving society" when it's found to be counter-productive, or do we keep going forward with improving society and hope things get better? Trying to "break out of the status quo" to produce more a accurate representation of the world sounds great, I'm totally all for removing human biases, but my concern is that it'll be handled badly to mistakenly attempt to benefit certain classes of society (eg: repeat of subprime mortgage crises from 07-09 but much more difficult to diagnose).
- tptacek 10y ago
  I'm not sure you understand my point, because you keep talking about "progressive slant", and I keep trying to explain to you that ML is going to be just as hard --- in fact, probably far harsher --- on "red state" voters. Think of it in terms of survival bias. ML models that discriminate against the well-educated urban workers that design and deploy them are not going to make it to production if they obviously discriminate against their own designers. Those developers and "data scientists" are not going to notice if those same models happen to deny college admission to kids who took a year off after graduating to work at a factory to help pay for their siblings room and board.
  - ihsw 10y ago
    That is actually a good analogy. Thank you for that.