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This 'algorithm has bias' kind of headline needs to die. Every system has '''bias''' in the colloquial sense so long as the inputs to that system are not unifo

This 'algorithm has bias' kind of headline needs to die. Every system has '''bias''' in the colloquial sense so long as the inputs to that system are not uniformly distributed. The only thing the designer of the system can do is make a conscious choice about which way to '''bias''' the system and be able to justify that choice. In practical terms it means that systems that deal with diverse populations are always going to be 'unfair' towards some group of people. The only control we have is who that group of people will be. This is inherent and unavoidable. We need to accept this fact and keep it in consideration when designing such systems (or choosing not to design them in the first place). {procedural fairness, group fairness, utility}. Pick two. https://www.youtube.com/watch?v=Zn7oWIhFffs https://www.youtube.com/watch?v=Zn7oWIhFffs

solotronics 7y ago
It comes down to the world frame some folks have. To them the world should have total fairness between all different people irregardless of predisposition, skill, situational luck, or effort put in. If it is not fair by their system they can fix it by pressing down on the scale. If any system or person has a fact that disrupts their worldview they are labeled things like "biased/racist/patriarchal/etc". Their desire for absolute equity requires rejecting facts that show people are different. To people with this worldview an algorithm will always be "biased" because it simply reflects statistics instead of their view of they see as "right" which is with their benevolent finger on the scale. I edited this slightly to try and remove any us vs them rhetoric. I am not passing any judgement just stating what I have observed.
- cycrutchfield 7y ago
  That’s gonna be a yikes from me, dog. Edited this (since you did as well) to say that simply pointing to statistics and saying that “facts don’t care about your feelings” is sophomoric and probably intentionally disingenuous. For instance, statistically in the US black people are more likely to commit crimes than white people. So should we implement systems that target suspects based on the color of their skin? Obviously not. There is a very important confounding factor, socioeconomic status, which happens to be very correlated with skin color in the US due to a long history of racial discrimination. So saying that “these systems should not be racially biased” is an important criteria.
  - 8f2ab37a-ed6c 7y ago
    We already discriminate as of today based on gender statistics in areas such as car insurance, where men pay more on average, because they're statistically more likely to cause accidents and drive recklessly. Why can't statistics be extended to other areas?
    - chronic829 7y ago
      Also, men dominate the trucking and construction industry. Why are we not pushing for gender equality there? Hint: the answer is money.
    - mantap 7y ago
      In the EU exactly that kind of gender discrimination is illegal. Insurers are not allowed to charge men more than women, just for being men. The question is, do you want to live in a society where prices are set based on one's genes. I sure don't.
      - briandear 7y ago
        So what the EU does is just charge more for everyone which means women pay higher rates for car insurance despite having lower accident rates. So in the goal of equality, women are being forced to pay more than they should based on their level of risk. That’s exactly the opposite of discrimination — women pay more despite lower risk, which makes that EU law, discriminatory.
      - missosoup 7y ago
        In the age of ML this is almost impossible to police and very difficult to implement even if you want to. You blind your model to gender but it learns to infer it from names. You blind it to names, it learns to infer it from other inputs. By the time you've blinded your model to anything that gives away gender, it no longer has utility. If you instead intentionally alter the model to still have utility but impact both genders equally, it's no longer procedurally fair and does in fact discriminate. This is the trilemma mentioned above. You must choose which of those factors you care about and which one you'll sacrifice. If you're unable to make that choice and morally stand behind it, then probably you shouldn't be building said model/system in the first place. This is one of the major topics of the ML ethics conversation.
jakelazaroff 7y ago
You're right, in that there is a tension between systems behaving equally and equitably, and that either one can be seen as "bias". (Although there can of course be systems that by design behave neither equally nor equitably, whether intentional or not.) I think the "algorithm has bias" type of headlines proliferate because there's a common misconception that algorithms are somehow "objective". See for example Ryan Saavedra implying algorithms can't be racist because they're "driven by math": https://twitter.com/realsaavedra/status/1087627739861897216?lang=en https://twitter.com/realsaavedra/status/1087627739861897216?...

solotronics 7y ago

It comes down to the world frame some folks have. To them the world should have total fairness between all different people irregardless of predisposition, skill, situational luck, or effort put in. If it is not fair by their system they can fix it by pressing down on the scale. If any system or person has a fact that disrupts their worldview they are labeled things like "biased/racist/patriarchal/etc". Their desire for absolute equity requires rejecting facts that show people are different. To people with this worldview an algorithm will always be "biased" because it simply reflects statistics instead of their view of they see as "right" which is with their benevolent finger on the scale. I edited this slightly to try and remove any us vs them rhetoric. I am not passing any judgement just stating what I have observed.

cycrutchfield 7y ago
That’s gonna be a yikes from me, dog. Edited this (since you did as well) to say that simply pointing to statistics and saying that “facts don’t care about your feelings” is sophomoric and probably intentionally disingenuous. For instance, statistically in the US black people are more likely to commit crimes than white people. So should we implement systems that target suspects based on the color of their skin? Obviously not. There is a very important confounding factor, socioeconomic status, which happens to be very correlated with skin color in the US due to a long history of racial discrimination. So saying that “these systems should not be racially biased” is an important criteria.
- 8f2ab37a-ed6c 7y ago
  We already discriminate as of today based on gender statistics in areas such as car insurance, where men pay more on average, because they're statistically more likely to cause accidents and drive recklessly. Why can't statistics be extended to other areas?
  - chronic829 7y ago
    Also, men dominate the trucking and construction industry. Why are we not pushing for gender equality there? Hint: the answer is money.
  - mantap 7y ago
    In the EU exactly that kind of gender discrimination is illegal. Insurers are not allowed to charge men more than women, just for being men. The question is, do you want to live in a society where prices are set based on one's genes. I sure don't.
    - briandear 7y ago
      So what the EU does is just charge more for everyone which means women pay higher rates for car insurance despite having lower accident rates. So in the goal of equality, women are being forced to pay more than they should based on their level of risk. That’s exactly the opposite of discrimination — women pay more despite lower risk, which makes that EU law, discriminatory.
    - missosoup 7y ago
      In the age of ML this is almost impossible to police and very difficult to implement even if you want to. You blind your model to gender but it learns to infer it from names. You blind it to names, it learns to infer it from other inputs. By the time you've blinded your model to anything that gives away gender, it no longer has utility. If you instead intentionally alter the model to still have utility but impact both genders equally, it's no longer procedurally fair and does in fact discriminate. This is the trilemma mentioned above. You must choose which of those factors you care about and which one you'll sacrifice. If you're unable to make that choice and morally stand behind it, then probably you shouldn't be building said model/system in the first place. This is one of the major topics of the ML ethics conversation.

jakelazaroff 7y ago

You're right, in that there is a tension between systems behaving equally and equitably, and that either one can be seen as "bias". (Although there can of course be systems that by design behave neither equally nor equitably, whether intentional or not.) I think the "algorithm has bias" type of headlines proliferate because there's a common misconception that algorithms are somehow "objective". See for example Ryan Saavedra implying algorithms can't be racist because they're "driven by math": https://twitter.com/realsaavedra/status/1087627739861897216?lang=en https://twitter.com/realsaavedra/status/1087627739861897216?...