11 ms·

AI Explorables: big ideas in machine learning, simply explained

165 points by smitop 5y ago

Graffur 5y ago
In short: garbage in, garbage out
- azinman2 5y ago
  That’s actually not a good summary at all. You could have quality in, but apply to a different group than was trained on, and get garbage out (eg face recognition trained on one set of races or genders, then try to apply to different races to genders). Or you train on data that’s high quality but is the result of inequity (eg police profiling, or credit worthiness), and then you get inequity out of the model.
  - nerdponx 5y ago
    I think it's fair to say that one component of AI ethics is identifying these subtle forms of "garbage out" and the subtle forms of "garbage in" that cause them.
    - azinman2 5y ago
      Garbage in means you have too much noise over signal coming in. You might be able to call current society garbage if you wish, but from an ML perspective it’s not accurate to say arbitrary input is garbage if it indeed is high quality data that reflects society as it is, or if you only have data on a certain set of classes but what you have is noiseless.
      - nerdponx 5y ago
        If you are studying criminality and using conviction record data, I think it's fair to say that the biased introduced by biased policing, biased laws, etc. is a form of "garbage-ness" in the data, in that it might not reflect the reality that you were trying to model.
      - Graffur 5y ago
        Biased data is garbage if your aim is to have unbiased results...
biasedbrain 5y ago
These are now the "big ideas" in machine learning? I guess they have given up on AGI then? In my book, this AI diversity nonsense is not even AI research. The SJWs pretend they discovered the concept of bias, when it has always been a core part of Machine Learning from the beginning. Even more concerning, it is an attempt to permanently encode their distorted sense of reality in AI models that affect billions of people. This needs to be fought, not celebrated.
- jijji 5y ago
  This distorted sense of reality is already visible in Google's algorithms. Search for "men can" or "women can" on google home page, only to find "men can have periods", "men can get pregnant". Rather than have ML models trained on data that respresents the reality of something, the data is changed or the model is changed to spit out this distorted reality that doesnt exist in the model or data...
  - nl 5y ago
    This is a weird claim! I searched for "men can". Eventually, on the fourth page of results I found an article from healthline[1] about periodic hormonal changes in men that does appear to have similar effects to the female period. This doesn't appear to be any form of bias or correction of bias or anything other than a highly SEOed and indexed page the is shown in response to a very vague query that matches the title of this page. [1] https://www.healthline.com/health/do-men-have-periods https://www.healthline.com/health/do-men-have-periods
    - jijji 5y ago
      I am not talking about search results, rather Google Instant results [0] .... [0] https://m.imgur.com/a/zMI6pTl https://m.imgur.com/a/zMI6pTl
- pepemysurprised 5y ago
  This nonsense doesn't even have to do anything with AI, it's just PR and a response to the current cultural environment.
throwaway789257 5y ago
I clicked expected to see big ML ideas explained, and all I found was Google scrubbing its AI ethics reputation after the debacle last year. I hope they devote their energies to actually explaining some ideas about ML rather than nattering on about fairness.
daenz 5y ago
Nobody is actually smarter or better than anyone at anything. It's a result of biases and privilege. And if all biases were taken into consideration, everyone would (and should) score exactly the same on everything, regardless of age, sex, race, gender, ethnicity, orientation, etc etc. That's the underlying message about ML initiatives that touch sensitive topics. Yes, we should be very careful not to encode biases into our models, and these biases are definitely real. Absolutely 100%! But what happens if the models still show disparity? Are we prepared for that possibility? Does it mean there are more biases yet to uncover and we must root them out? Or should we hide anything that reveals disparity in the absence of biases? Or are all biases impossible to remove, so they must forever remain the explanation for all disparity? Is disparity a proof of bias? Because if that is true, then the only conclusion is that every person is exactly functionally equal and the only differences are human biases. I've seen smart tech people unable to say that men are on average taller than women because (I believe) they were afraid to say that some "favorable" outcome (tall) was a result of some component that could be genetic. Their argument was anecdotes of tall women, and saying there is no such thing as an "average" man or "average" woman.
- j7ake 5y ago
  It would be nice to do a well-designed experiment to ask what are the biological differences in different categories of humans (eg old vs young). Then use that ground truth to assess whether an ML model is generating the biases that deviate from the ground truth. That way there is no a priori assumption of what is technical bias and what is real signal. With the level of investment people are putting into ML technologies, these types of ground truth calibrations would cost only a fraction of the total budget.
- joe_the_user 5y ago
  Nobody is actually smarter or better than anyone at anything. It's a result of biases and privilege. Where is the linked article claiming this? Your post seems like complete derail of the article discussion for the purposes of making broader political points. [insert hn caveat about being more nuanced in potentially more flamebait topic]
- spoonjim 5y ago
sharikous 5y ago
God know how nice explanations of ideas in ML would be useful. But this is ML enforcement of made up "social values", like diversity and fairness
- nerdponx 5y ago
  God forbid I care about other people!
deleted 5y ago
[deleted]
antonzabirko 5y ago
Cool idea but terrible topics
tomrod 5y ago
/director hat engaged I'm really surprised that most of the top level comments are so negative. This is a huge discussion in using ML for policy. In five to ten years time you'll have a hard time using ML in business without having the concepts these items address. Will you ML model result in incidental bias to people with criminal records? The disabled? The elderly and other protected categories? Does your insurance company require certain fairness metrics be deployed to maintain your product or service policy coverage? Will your company pursue tax incentives that focus on certain definitions of unbiasedness? Developers, data scientists, and statisticians have had a good run implementing loads of statistical models without considering the negative implications/externalities of automated decision rules and paths to KPIs. We will likely see this required in the future. It will be hard. But, it will be hard to automate away for the foreseeable future (Github Copilot made a serious POC impact for standard dev work!). /throws director hat back onto the hat rack, back to data science for me
- biasedbrain 5y ago
  If an elderly person will cost your insurance more than a younger person, the ML should tell you so. Period. If by policy you should be forced to still give them the same conditions, that should be decided on a different level, not at the level of the tool that analyzes your data.
- nerdponx 5y ago
  > I'm really surprised that most of the top level comments are so negative. Lots of throwaway accounts here, and the comments are clearly written by people who aren't professionals in this field. Just downvote them and move on. > Does your insurance company require certain fairness metrics be deployed to maintain your product or service policy coverage? Will your company pursue tax incentives that focus on certain definitions of unbiasedness? These are scary thoughts. If this kind of policy does come to pass, I fear it would backfire significantly, basically making any machine learning (let alone "AI") a serious legal liability. Imagine world in which companies no longer want to hire data scientists of the regulatory risk of even having one on staff! There's an argument to be made that a bit of ML liability is a good thing, that important decisions shouldn't be made by machine. I'm tempted to agree in most cases. But encoding it into regulation will be extremely difficult, even if you have truly pure and noble intentions, without making a giant mess. And that's before you realize that, obviously, major "AI powers" like Google, Facebook, etc. could have been working on this for years, and as soon as it's ready for production they will start lobbying aggressively for it, in order to further monopolize the ML/AI field. Also, do the machines actually perform worse (i.e. "more biased") than underpaid humans who are prone to fatique, mistakes, and laziness?
fumeux_fume 5y ago
It's a trap
pepemysurprised 5y ago
AI Explorables: Big ideas in PR to make Google look like a good-willed company. This only exists because someone needed to get promoted for working on "diversity efforts"

165 points by smitop 5y ago

Graffur 5y ago

In short: garbage in, garbage out

azinman2 5y ago
That’s actually not a good summary at all. You could have quality in, but apply to a different group than was trained on, and get garbage out (eg face recognition trained on one set of races or genders, then try to apply to different races to genders). Or you train on data that’s high quality but is the result of inequity (eg police profiling, or credit worthiness), and then you get inequity out of the model.
- nerdponx 5y ago
  I think it's fair to say that one component of AI ethics is identifying these subtle forms of "garbage out" and the subtle forms of "garbage in" that cause them.
  - azinman2 5y ago
    Garbage in means you have too much noise over signal coming in. You might be able to call current society garbage if you wish, but from an ML perspective it’s not accurate to say arbitrary input is garbage if it indeed is high quality data that reflects society as it is, or if you only have data on a certain set of classes but what you have is noiseless.
    - nerdponx 5y ago
      If you are studying criminality and using conviction record data, I think it's fair to say that the biased introduced by biased policing, biased laws, etc. is a form of "garbage-ness" in the data, in that it might not reflect the reality that you were trying to model.
    - Graffur 5y ago
      Biased data is garbage if your aim is to have unbiased results...

biasedbrain 5y ago

These are now the "big ideas" in machine learning? I guess they have given up on AGI then? In my book, this AI diversity nonsense is not even AI research. The SJWs pretend they discovered the concept of bias, when it has always been a core part of Machine Learning from the beginning. Even more concerning, it is an attempt to permanently encode their distorted sense of reality in AI models that affect billions of people. This needs to be fought, not celebrated.

jijji 5y ago
This distorted sense of reality is already visible in Google's algorithms. Search for "men can" or "women can" on google home page, only to find "men can have periods", "men can get pregnant". Rather than have ML models trained on data that respresents the reality of something, the data is changed or the model is changed to spit out this distorted reality that doesnt exist in the model or data...
- nl 5y ago
  This is a weird claim! I searched for "men can". Eventually, on the fourth page of results I found an article from healthline[1] about periodic hormonal changes in men that does appear to have similar effects to the female period. This doesn't appear to be any form of bias or correction of bias or anything other than a highly SEOed and indexed page the is shown in response to a very vague query that matches the title of this page. [1] https://www.healthline.com/health/do-men-have-periods https://www.healthline.com/health/do-men-have-periods
  - jijji 5y ago
    I am not talking about search results, rather Google Instant results [0] .... [0] https://m.imgur.com/a/zMI6pTl https://m.imgur.com/a/zMI6pTl
pepemysurprised 5y ago
This nonsense doesn't even have to do anything with AI, it's just PR and a response to the current cultural environment.

throwaway789257 5y ago

I clicked expected to see big ML ideas explained, and all I found was Google scrubbing its AI ethics reputation after the debacle last year. I hope they devote their energies to actually explaining some ideas about ML rather than nattering on about fairness.

daenz 5y ago

Nobody is actually smarter or better than anyone at anything. It's a result of biases and privilege. And if all biases were taken into consideration, everyone would (and should) score exactly the same on everything, regardless of age, sex, race, gender, ethnicity, orientation, etc etc. That's the underlying message about ML initiatives that touch sensitive topics. Yes, we should be very careful not to encode biases into our models, and these biases are definitely real. Absolutely 100%! But what happens if the models still show disparity? Are we prepared for that possibility? Does it mean there are more biases yet to uncover and we must root them out? Or should we hide anything that reveals disparity in the absence of biases? Or are all biases impossible to remove, so they must forever remain the explanation for all disparity? Is disparity a proof of bias? Because if that is true, then the only conclusion is that every person is exactly functionally equal and the only differences are human biases. I've seen smart tech people unable to say that men are on average taller than women because (I believe) they were afraid to say that some "favorable" outcome (tall) was a result of some component that could be genetic. Their argument was anecdotes of tall women, and saying there is no such thing as an "average" man or "average" woman.

j7ake 5y ago
It would be nice to do a well-designed experiment to ask what are the biological differences in different categories of humans (eg old vs young). Then use that ground truth to assess whether an ML model is generating the biases that deviate from the ground truth. That way there is no a priori assumption of what is technical bias and what is real signal. With the level of investment people are putting into ML technologies, these types of ground truth calibrations would cost only a fraction of the total budget.
joe_the_user 5y ago
Nobody is actually smarter or better than anyone at anything. It's a result of biases and privilege. Where is the linked article claiming this? Your post seems like complete derail of the article discussion for the purposes of making broader political points. [insert hn caveat about being more nuanced in potentially more flamebait topic]
spoonjim 5y ago

sharikous 5y ago

God know how nice explanations of ideas in ML would be useful. But this is ML enforcement of made up "social values", like diversity and fairness

nerdponx 5y ago
God forbid I care about other people!

deleted 5y ago

[deleted]

antonzabirko 5y ago

Cool idea but terrible topics

tomrod 5y ago

/director hat engaged I'm really surprised that most of the top level comments are so negative. This is a huge discussion in using ML for policy. In five to ten years time you'll have a hard time using ML in business without having the concepts these items address. Will you ML model result in incidental bias to people with criminal records? The disabled? The elderly and other protected categories? Does your insurance company require certain fairness metrics be deployed to maintain your product or service policy coverage? Will your company pursue tax incentives that focus on certain definitions of unbiasedness? Developers, data scientists, and statisticians have had a good run implementing loads of statistical models without considering the negative implications/externalities of automated decision rules and paths to KPIs. We will likely see this required in the future. It will be hard. But, it will be hard to automate away for the foreseeable future (Github Copilot made a serious POC impact for standard dev work!). /throws director hat back onto the hat rack, back to data science for me

biasedbrain 5y ago
If an elderly person will cost your insurance more than a younger person, the ML should tell you so. Period. If by policy you should be forced to still give them the same conditions, that should be decided on a different level, not at the level of the tool that analyzes your data.
nerdponx 5y ago
> I'm really surprised that most of the top level comments are so negative. Lots of throwaway accounts here, and the comments are clearly written by people who aren't professionals in this field. Just downvote them and move on. > Does your insurance company require certain fairness metrics be deployed to maintain your product or service policy coverage? Will your company pursue tax incentives that focus on certain definitions of unbiasedness? These are scary thoughts. If this kind of policy does come to pass, I fear it would backfire significantly, basically making any machine learning (let alone "AI") a serious legal liability. Imagine world in which companies no longer want to hire data scientists of the regulatory risk of even having one on staff! There's an argument to be made that a bit of ML liability is a good thing, that important decisions shouldn't be made by machine. I'm tempted to agree in most cases. But encoding it into regulation will be extremely difficult, even if you have truly pure and noble intentions, without making a giant mess. And that's before you realize that, obviously, major "AI powers" like Google, Facebook, etc. could have been working on this for years, and as soon as it's ready for production they will start lobbying aggressively for it, in order to further monopolize the ML/AI field. Also, do the machines actually perform worse (i.e. "more biased") than underpaid humans who are prone to fatique, mistakes, and laziness?

fumeux_fume 5y ago

It's a trap

pepemysurprised 5y ago

AI Explorables: Big ideas in PR to make Google look like a good-willed company. This only exists because someone needed to get promoted for working on "diversity efforts"