6 ms·

Men measurably impact the bottom line of insurance companies more. Actuarial math is not 'AI', there's no 'magnification of bias'. The whole point of the calcul

by quxbar 8y ago

Men measurably impact the bottom line of insurance companies more. Actuarial math is not 'AI', there's no 'magnification of bias'. The whole point of the calculation is to eliminate bias so that the company makes sound decisions. Please don't generalize things that you haven't made an effort to understand. When you say stuff like this, you're making it harder to fight actual bias in the world, because you make people not take your point seriously.

jedberg 8y ago
I have a pretty deep understanding actually. Please explain to me how making an actuarial table with different data sets would get you the same answer without bias.
- lykr0n 8y ago
  Bias, according to google: > prejudice in favor of or against one thing, person, or group compared with another, usually in a way considered to be unfair. Data can't be bias. It can be a misrepresentation or inaccurate, but not bias. If I have an accurate data-set that say ethnic group X in area Y has a higher rate of violence, and because of that I make the choice to charge higher premiums for group X- is that bias? No. You can't scream bias when you don't like the conclusions the data draws. Drawing conclusions from data isn't bias-ed(?) but using only that data might be.
  - jedberg 8y ago
    So if I gave you a dataset that only had women in it, and from that you concluded that men never have accidents and should therefore have a $0 insurance rate, you wouldn't say that the data was biased against women?
    - lykr0n 8y ago
      I would say the data is inaccurate.
      - jedberg 8y ago
        Given that it is impossible to have a 100% accurate dataset for real world events, wouldn't every dataset be inaccurate to some measure? And wouldn't the level of inaccuracy represent a bias of some sort?
        lykr0n 8y ago
        There is a whole field of mathematics on how to figure this out. https://en.wikipedia.org/wiki/Sample_size_determination https://en.wikipedia.org/wiki/Sample_size_determination And yes, nothing is 100% accurate. The census isn't 100% accurate, but it's good enough. A 95% accuracy is the generally accepted target, which is what you have a statically significant. If you have a target population of 330 Million people, you only need 40k people to hit a 95% confidence value with a 0.1 confidence interval. Again, this has all been figured out, formulated, and settled. You keep using the word bias and I don't think you know what it means. If the data is inaccurate, it's inaccurate. If I wanted to sample the US population, but only used people from Long Island and only polled 10 people, that's not a bias dataset- it's inaccurate. Conclusions drawn from inaccurate data are inaccurate. Bias data would be statistics cherry-picked to showcase one view point over another. The dataset itself isn't bias, but maybe the presentation is. As I said before, good (and this is important) data can't be bias- what you do with it can be. Saying "Men are more likely to be bad drivers" is a factual statement. "All Men are bad drivers" is a bias one. See the difference?
        jedberg 8y ago
        You said: Saying "Men are more likely to be bad drivers" is a factual statement. "All Men are bad drivers" is a bias one. Let's change it up a little. Take the statement "Black men are more likely to be criminals than white men". Is that factually true? A dataset with a statistically significant sample would say yes, in fact black men are incarcerated at a higher rate per capita than white men. But look how that data came to be -- by police making arrests. And there is also plenty of data out there showing that black men get arrested more often than white men for the same crimes, and get longer sentences for the same crimes than white men. So clearly, the dataset that shows black men are more likely to be criminals is biased, even though it is accurate. Now imagine building an AI (or actuarial table) based on that data. It would necessarily identify black men as "more likely to be a criminal" simply based on the fact that they are black. So now you've magnified the bias in the data. So yes, the data is accurate but that doesn't mean it isn't biased too.
  - natrik 8y ago
    Technically data can be biased because what you call a misrepresentation is termed bias: https://en.wikipedia.org/wiki/Sampling_bias https://en.wikipedia.org/wiki/Sampling_bias https://en.wikipedia.org/wiki/Selection_bias https://en.wikipedia.org/wiki/Selection_bias
    - lykr0n 8y ago
      True. Inaccurate data can be bias, but I think made that distinction. If you have a representative, statically accurate data set, you can't call the data bias if it points one way or another.
    - DoctorOetker 8y ago
      Don't forget treatment bias, also known as "nature versus nurture". One can totally remove sampling and selection bias by tallying the whole population in the measurement, say in antiquity. I predict the data (which can't have sampling and selection bias) would describe how slaves have a higher injury and mortality rate: after whipping or overworking or being fed inferior food, the injured are last in line when the food is dispensed etc. This is real world treatment bias. How just is it to increase say insurance rate for the slaves? (I know they probably didn't have insurance in antiquity...) Edit: In other words, statistics done correctly (i.e. representatively) on the real world can tell us what the real world (and its status quo) looks like, but tells us nothing about the ethics of the situation.

by quxbar 8y ago

jedberg 8y ago

I have a pretty deep understanding actually. Please explain to me how making an actuarial table with different data sets would get you the same answer without bias.

lykr0n 8y ago
Bias, according to google: > prejudice in favor of or against one thing, person, or group compared with another, usually in a way considered to be unfair. Data can't be bias. It can be a misrepresentation or inaccurate, but not bias. If I have an accurate data-set that say ethnic group X in area Y has a higher rate of violence, and because of that I make the choice to charge higher premiums for group X- is that bias? No. You can't scream bias when you don't like the conclusions the data draws. Drawing conclusions from data isn't bias-ed(?) but using only that data might be.
- jedberg 8y ago
  So if I gave you a dataset that only had women in it, and from that you concluded that men never have accidents and should therefore have a $0 insurance rate, you wouldn't say that the data was biased against women?
  - lykr0n 8y ago
    I would say the data is inaccurate.
    - jedberg 8y ago
      Given that it is impossible to have a 100% accurate dataset for real world events, wouldn't every dataset be inaccurate to some measure? And wouldn't the level of inaccuracy represent a bias of some sort?
      - lykr0n 8y ago
        There is a whole field of mathematics on how to figure this out. https://en.wikipedia.org/wiki/Sample_size_determination https://en.wikipedia.org/wiki/Sample_size_determination And yes, nothing is 100% accurate. The census isn't 100% accurate, but it's good enough. A 95% accuracy is the generally accepted target, which is what you have a statically significant. If you have a target population of 330 Million people, you only need 40k people to hit a 95% confidence value with a 0.1 confidence interval. Again, this has all been figured out, formulated, and settled. You keep using the word bias and I don't think you know what it means. If the data is inaccurate, it's inaccurate. If I wanted to sample the US population, but only used people from Long Island and only polled 10 people, that's not a bias dataset- it's inaccurate. Conclusions drawn from inaccurate data are inaccurate. Bias data would be statistics cherry-picked to showcase one view point over another. The dataset itself isn't bias, but maybe the presentation is. As I said before, good (and this is important) data can't be bias- what you do with it can be. Saying "Men are more likely to be bad drivers" is a factual statement. "All Men are bad drivers" is a bias one. See the difference?
        jedberg 8y ago
        You said: Saying "Men are more likely to be bad drivers" is a factual statement. "All Men are bad drivers" is a bias one. Let's change it up a little. Take the statement "Black men are more likely to be criminals than white men". Is that factually true? A dataset with a statistically significant sample would say yes, in fact black men are incarcerated at a higher rate per capita than white men. But look how that data came to be -- by police making arrests. And there is also plenty of data out there showing that black men get arrested more often than white men for the same crimes, and get longer sentences for the same crimes than white men. So clearly, the dataset that shows black men are more likely to be criminals is biased, even though it is accurate. Now imagine building an AI (or actuarial table) based on that data. It would necessarily identify black men as "more likely to be a criminal" simply based on the fact that they are black. So now you've magnified the bias in the data. So yes, the data is accurate but that doesn't mean it isn't biased too.
- natrik 8y ago
  Technically data can be biased because what you call a misrepresentation is termed bias: https://en.wikipedia.org/wiki/Sampling_bias https://en.wikipedia.org/wiki/Sampling_bias https://en.wikipedia.org/wiki/Selection_bias https://en.wikipedia.org/wiki/Selection_bias
  - lykr0n 8y ago
    True. Inaccurate data can be bias, but I think made that distinction. If you have a representative, statically accurate data set, you can't call the data bias if it points one way or another.
  - DoctorOetker 8y ago
    Don't forget treatment bias, also known as "nature versus nurture". One can totally remove sampling and selection bias by tallying the whole population in the measurement, say in antiquity. I predict the data (which can't have sampling and selection bias) would describe how slaves have a higher injury and mortality rate: after whipping or overworking or being fed inferior food, the injured are last in line when the food is dispensed etc. This is real world treatment bias. How just is it to increase say insurance rate for the slaves? (I know they probably didn't have insurance in antiquity...) Edit: In other words, statistics done correctly (i.e. representatively) on the real world can tell us what the real world (and its status quo) looks like, but tells us nothing about the ethics of the situation.