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> Since Amazon’s current employee base skews male, the examples of ‘successful hires’ also, mechanistically, skewed male and so, therefore, did this system’s se

> Since Amazon’s current employee base skews male, the examples of ‘successful hires’ also, mechanistically, skewed male and so, therefore, did this system’s selection of resumés. Amazon spotted this and the system was never put into production. Couldn't they have retrained the system with a 50/50 mix of males / females resumes ? Or restrict the use of the algorithm to sort male resumes ? Or maybe resumes don't actually correlate at all with success in Amazon ...

deogeo 7y ago
Sounds like an extraordinarily poor AI system if it depends on absolute numbers, and not per capita. And wouldn't the number of unsuccessful hires also skew male?
- theoh 7y ago
  Consider the possibility that the (pre-AI system) probability of success for a female applicant is the same as the probability of success of a male applicant. You could make a "per capita" quota as a kind of goal. That's not a problem, but how would you make sure the quota was met? The typical AI system doesn't work on the basis of selecting candidates entirely at random, pro rata, in order to meet a quota. It works on the basis of criteria for success. One thing it might learn (unfortunately) is that most posts at the company are filled by men.
  - cygaril 7y ago
    From a machine learning point of view, one can just add the constraint that the probability of being in the "yes" bucket is that same for both male and female candidates. Doing this will give a worse fit than an unconstrained optimization, but it is fairer. More sophisticated approaches are possible.
    - theoh 7y ago
      There's no "just" to any aspect of this topic. I think what you are talking about is what is sometimes called "classification parity", and there are problems with it, and with everything else we've come up with to combat bias. https://arxiv.org/abs/1808.00023 https://arxiv.org/abs/1808.00023
- jerf 7y ago
  "Sounds like an extraordinarily poor AI system if it depends on absolute numbers, and not per capita." To some extent, you're bringing in your human bias to prefer human biases when you make that statement. We humans have a hierarchy of important attributes, and for various reasons believe race and gender are more important than eye color or height. But the machine learning algorithm just gets a multidimensional point in hyperspace. It doesn't, a priori, "know" that it needs to do a "per capita" adjustment based on FIELD_1 any more than it knows it needs to do a per capita adjustment on FIELD_2. And you can't "adjust" on all the fields because that'll just cancel out. We are also in the weird position of wanting the machine to do adjustments based on FIELD_1, but without us having to actually admit to ourselves that we're doing it. From a technical perspective, probably the best answer is to do a straight-up training based on the data, then have an cleanly-separated after-the-fact cleanup process to perform whatever social adjustments it is we want on the outcome. But nobody is willing to admit that's what we want, and to put those adjustments down on paper in the form of code, because the instant they're concrete, pretty much everybody is going to decide they're wrong, and no two people are going to agree on the manner in which they are wrong, and an epic, national-front-page-news shitstorm will ensue. So here we are, trying to make adjustments without making adjustments, or, alternatively, trying to make adjustments in a place where we can blame the AI rather than humans. (The ironic thing is that because we can't admit what we're trying to do, we're going to end up doing a really poor job of it. Tools will be applied haphazardly, the results can't be measured except very grossly at the very end of the process, and the goals won't be obtained and the system is always going to be quirky and weird. If we could clearly declare what it is we actually wanted, it would be fairly easy to get it from the AIs.)
- Bartweiss 7y ago
  The basic "resumes skewed male so the algorithm did too" explanation appears to be incorrect. But it's found in the original Reuters story and most derived stories, and finding it here implies it's reached the level of urban legend. Going by the details of the Reuters story and several others, it appears that what actually happened was a training/task mismatch. Amazon wanted an algorithm to do resume discovery, which recruiters would run and get quality predictions as they viewed resumes. But they trained it on resume results, giving it past resumes which had been submitted to Amazon and telling it to seek similar resumes. None of the stories make it clear if there even was negative training data; it looks like the tool was simply told to compute degree-of-similarity to past inputs, and possibly told to prioritize resumes which were ultimately hired. As a result, the tool was trying to convert a relatively gender-neutral pool (resumes found online) to a skewed one (Amazon applicant resumes), and did so by weighting gendered terms. It also seems to have underweighted technical terms, failing to appreciate them as mandatory or strictly position-specific. The developers were sufficiently aware of that to catch and correct the known gender biases (e.g. devaluing women's colleges or the literal word "women's"), but were scared there were other uncaught biases. And the results were apparently terrible all around, so the tool was scrapped. Which is pretty much what you'd expect from something trained on exclusively positive, sample-biased examples. The story has been seriously distorted, but the real plan also seems terrible...
duxup 7y ago
It does make you wonder how much AI will be .. AI and how much guidance for desired outcomes humans will give it. Humans are pretty happy to create nonsensical results if it fits their goals... especially if it befits them. I wonder if with AI we do that to the point that it is somewhat irrelevant.
TheRealPomax 7y ago
maybe, but this might also just be someone unwilling to commit to the sunk cost fallacy. You can spend time and money fixing it, or you can cut your losses and just stop trying to automate something that probably didn't need full automation to begin with.
FakeComments 7y ago
If the only problem is that it mixes up male and female resumes, why not have it sort each into two piles and have a human recruiter perform the merge by picking the better applicant from the pair on top, repeatedly? I have the suspicion that Amazons AI was factually correct, but politically wrong.
- TheCoelacanth 7y ago
  I highly doubt that the preference for male candidates was the only problem with the AI. The preference for candidates with ice hockey on their resume almost certainly also would have resulted in a preference for white candidates.
  - FakeComments 7y ago
    Sure, my point is why does that matter? The correlation with ice hockey could be a career relevant detail because of correlation with discipline and pain tolerance. If it tracks all such signals, I don’t see the problem. That it also has a circumstantial correlation with races isn’t inherently problematic — it could be that different groups of people are differently qualified. There seems to be a position that culture is uncorrelated to job success, and that’s just nonsense. When people can show me these biases are correlated to race or sex once controlling for culture, then we have a problem.
    - TheCoelacanth 7y ago
      Good luck with that argument in court.
    - michaelmior 7y ago
      Race and culture are two very different things. There certainly can be some correlation, but drawing conclusions of suitability for employment based on race is a problem.
      - FakeComments 7y ago
eanzenberg 7y ago
That wouldn't matter if the KPI (worker performance) being predicted, which is inherently biased as well, was distributed differently among the balanced pool of applicants.
munchbunny 7y ago
I think this retelling of the story is over-simplified. It's a compelling story, but I don't know any competent engineers who give up on a whole project because of one setback. If this system never saw production use, it was because it's still not ready, or there were many other issues that aren't mentioned that led the team to give up, or because political winds shifted. Amazon is famous for killing projects quickly.
manfredo 7y ago
The whole problem with allegations of AI bias is that people often point to disparities of outcome as proof of bias. The reality is that there are plenty of disparities on outcome regardless of bias, and the allegations of bias and attempt to rectify the alleged bias is another vector for the introduction of bias.
DuskStar 7y ago
One situation I could see leading to this result (Amazon cancelling their resume filtering software with the excuse that it 'skewed male') is that 1. The AI system accurately predicted employee success across both genders AND 2. The AI system predicted that women would do worse than men That's politically embarrassing and something that you can't necessarily 'fix' by improving the system. (see: all the 'will this person commit a crime if let out on parole' systems that end up accurately discriminating based on race) This isn't to say that women are worse engineers than men, or anything of that sort - only that the applicant pool to Amazon was skewed, or women were treated worse in the workplace and thus performed worse, or a dozen other possible causes. (And only in this hypothetical scenario! I have no inside info from Amazon!)
- gizmo686 7y ago
  Your example is quite possible, particularly at an organazation that would be embarrased by such a result. Assume that the ability curve of male applicants and female applicants are identical; that the majority of applicants are male; and that Amazon wants to hire more females then would be expected given the portion of applicants that are female. A natural way of accomplishing this goal is to give extra points to female applicants [0]. Due to selection bias, the ability curve of women within the population of Amazon engineers would skew lower then men within the population of Amazon engineers. This is a special case of a more general phenomona. If you have signal S that is positivly correlated with a desired trait in the general population, and over select for S, you will find that S is negativly correlated within your population. [0]. All proposals I have seen amount to either a good approximation of this or changing the applicant pool. And, by assumption, the latter is excluded.
- lotu 7y ago
  This is feels like an elephant in the room when it comes to AI bias. We develop an AI that accurately predicts outcomes and discover it is biased, then instead of asking if maybe this means our current system is deeply biased and needs to be changed, we say, "don't use the AI; keep using the people who might or might not be biased but we don't know because we can't measure it in the way an AI can be measured." If it isn't acceptable to use an AI to create biased outcomes how is it acceptable to use people to create the the same outcomes. AI decision making can be examined and tuned in ways that people cannot.
turtlecloud 7y ago
Just remove the gender/sex as variables for the AI and maybe name too. Preprocess the resumes to remove them. Now you remove the majority of gender bias for the AI.
- gizmo686 7y ago
  AI is really good at infering information. If gender is a real signal, it would be very difficult to filter the input such that it is not making a determination by what could be reffered to inferred gender.
Bartweiss 7y ago
This story has been constantly misrepresented, because Reuters absolutely botched their initial report. Amazon was never building a tool to decide which interviewed candidates to hire, they were building a tool for discovering candidates. It was biased, but that gender bias wasn't the proximate reason for scrapping the tool. As far as I can tell from later stories (e.g. 1, 2), what Amazon actually did was build a tool to show recruiters 'quality' predictions for all resumes, for instance as they scrolled LinkedIn. But they trained it on resumes submitted to Amazon for various positions, possibly also adding weight to resumes which produced hires. In which case the problem is painfully obvious; the system effectively had no negative training data, and its positive examples (submitted resumes) didn't actually match the desired output (qualified resumes). It was computing degree of similarity between a gender-neutral-ish pool (resumes posted online) and a gender-skewed pool (resumes submitted to Amazon), and tried to make that conversion with whatever data was available - like devaluing resumes that mentioned women's colleges. (This wasn't just a proxy-variable thing, the model essentially learned to weight on gender.) Amazon's team apparently caught this issue and did the usual things like blinding on those words. But they were scared of uncaught factors; reading between the lines, they were unable to "detrain" biases like neural nets do because their dataset and task didn't match. Ultimately, the tool was apparently scrapped because it made selections "almost at random". Which, again, isn't exactly surprising in light of the absolutely bonkers choice of training examples. [1] https://www.aclu.org/blog/womens-rights/womens-rights-workplace/why-amazons-automated-hiring-tool-discriminated-against https://www.aclu.org/blog/womens-rights/womens-rights-workpl... [2] https://www.ml.cmu.edu/news/news-archive/2018/october/amazon-scraps-secret-artificial-intelligence-recruiting-engine-that-showed-biases-against-women.html https://www.ml.cmu.edu/news/news-archive/2018/october/amazon...
bumby 7y ago
Or couldn't they provide data augmentation on the same samples to give the effect of a more diverse (and more populous) training set? Using the blog's skin cancer example, couldn't the labelled images be augmented by altering the skin tones and adding these new examples to the training set? It seems to me that some of the anomalous results discussed in the article are actually the result of poor model design or poor pre-processing data choices. We can't just throw anything to any ol' machine learning model and expect it to be magic

deogeo 7y ago

Sounds like an extraordinarily poor AI system if it depends on absolute numbers, and not per capita. And wouldn't the number of unsuccessful hires also skew male?

theoh 7y ago
Consider the possibility that the (pre-AI system) probability of success for a female applicant is the same as the probability of success of a male applicant. You could make a "per capita" quota as a kind of goal. That's not a problem, but how would you make sure the quota was met? The typical AI system doesn't work on the basis of selecting candidates entirely at random, pro rata, in order to meet a quota. It works on the basis of criteria for success. One thing it might learn (unfortunately) is that most posts at the company are filled by men.
- cygaril 7y ago
  From a machine learning point of view, one can just add the constraint that the probability of being in the "yes" bucket is that same for both male and female candidates. Doing this will give a worse fit than an unconstrained optimization, but it is fairer. More sophisticated approaches are possible.
  - theoh 7y ago
    There's no "just" to any aspect of this topic. I think what you are talking about is what is sometimes called "classification parity", and there are problems with it, and with everything else we've come up with to combat bias. https://arxiv.org/abs/1808.00023 https://arxiv.org/abs/1808.00023
jerf 7y ago
"Sounds like an extraordinarily poor AI system if it depends on absolute numbers, and not per capita." To some extent, you're bringing in your human bias to prefer human biases when you make that statement. We humans have a hierarchy of important attributes, and for various reasons believe race and gender are more important than eye color or height. But the machine learning algorithm just gets a multidimensional point in hyperspace. It doesn't, a priori, "know" that it needs to do a "per capita" adjustment based on FIELD_1 any more than it knows it needs to do a per capita adjustment on FIELD_2. And you can't "adjust" on all the fields because that'll just cancel out. We are also in the weird position of wanting the machine to do adjustments based on FIELD_1, but without us having to actually admit to ourselves that we're doing it. From a technical perspective, probably the best answer is to do a straight-up training based on the data, then have an cleanly-separated after-the-fact cleanup process to perform whatever social adjustments it is we want on the outcome. But nobody is willing to admit that's what we want, and to put those adjustments down on paper in the form of code, because the instant they're concrete, pretty much everybody is going to decide they're wrong, and no two people are going to agree on the manner in which they are wrong, and an epic, national-front-page-news shitstorm will ensue. So here we are, trying to make adjustments without making adjustments, or, alternatively, trying to make adjustments in a place where we can blame the AI rather than humans. (The ironic thing is that because we can't admit what we're trying to do, we're going to end up doing a really poor job of it. Tools will be applied haphazardly, the results can't be measured except very grossly at the very end of the process, and the goals won't be obtained and the system is always going to be quirky and weird. If we could clearly declare what it is we actually wanted, it would be fairly easy to get it from the AIs.)
Bartweiss 7y ago
The basic "resumes skewed male so the algorithm did too" explanation appears to be incorrect. But it's found in the original Reuters story and most derived stories, and finding it here implies it's reached the level of urban legend. Going by the details of the Reuters story and several others, it appears that what actually happened was a training/task mismatch. Amazon wanted an algorithm to do resume discovery, which recruiters would run and get quality predictions as they viewed resumes. But they trained it on resume results, giving it past resumes which had been submitted to Amazon and telling it to seek similar resumes. None of the stories make it clear if there even was negative training data; it looks like the tool was simply told to compute degree-of-similarity to past inputs, and possibly told to prioritize resumes which were ultimately hired. As a result, the tool was trying to convert a relatively gender-neutral pool (resumes found online) to a skewed one (Amazon applicant resumes), and did so by weighting gendered terms. It also seems to have underweighted technical terms, failing to appreciate them as mandatory or strictly position-specific. The developers were sufficiently aware of that to catch and correct the known gender biases (e.g. devaluing women's colleges or the literal word "women's"), but were scared there were other uncaught biases. And the results were apparently terrible all around, so the tool was scrapped. Which is pretty much what you'd expect from something trained on exclusively positive, sample-biased examples. The story has been seriously distorted, but the real plan also seems terrible...

duxup 7y ago

It does make you wonder how much AI will be .. AI and how much guidance for desired outcomes humans will give it. Humans are pretty happy to create nonsensical results if it fits their goals... especially if it befits them. I wonder if with AI we do that to the point that it is somewhat irrelevant.

TheRealPomax 7y ago

maybe, but this might also just be someone unwilling to commit to the sunk cost fallacy. You can spend time and money fixing it, or you can cut your losses and just stop trying to automate something that probably didn't need full automation to begin with.

FakeComments 7y ago

If the only problem is that it mixes up male and female resumes, why not have it sort each into two piles and have a human recruiter perform the merge by picking the better applicant from the pair on top, repeatedly? I have the suspicion that Amazons AI was factually correct, but politically wrong.

TheCoelacanth 7y ago
I highly doubt that the preference for male candidates was the only problem with the AI. The preference for candidates with ice hockey on their resume almost certainly also would have resulted in a preference for white candidates.
- FakeComments 7y ago
  Sure, my point is why does that matter? The correlation with ice hockey could be a career relevant detail because of correlation with discipline and pain tolerance. If it tracks all such signals, I don’t see the problem. That it also has a circumstantial correlation with races isn’t inherently problematic — it could be that different groups of people are differently qualified. There seems to be a position that culture is uncorrelated to job success, and that’s just nonsense. When people can show me these biases are correlated to race or sex once controlling for culture, then we have a problem.
  - TheCoelacanth 7y ago
    Good luck with that argument in court.
  - michaelmior 7y ago
    Race and culture are two very different things. There certainly can be some correlation, but drawing conclusions of suitability for employment based on race is a problem.
    - FakeComments 7y ago

eanzenberg 7y ago

That wouldn't matter if the KPI (worker performance) being predicted, which is inherently biased as well, was distributed differently among the balanced pool of applicants.

munchbunny 7y ago

I think this retelling of the story is over-simplified. It's a compelling story, but I don't know any competent engineers who give up on a whole project because of one setback. If this system never saw production use, it was because it's still not ready, or there were many other issues that aren't mentioned that led the team to give up, or because political winds shifted. Amazon is famous for killing projects quickly.

manfredo 7y ago

The whole problem with allegations of AI bias is that people often point to disparities of outcome as proof of bias. The reality is that there are plenty of disparities on outcome regardless of bias, and the allegations of bias and attempt to rectify the alleged bias is another vector for the introduction of bias.

DuskStar 7y ago

One situation I could see leading to this result (Amazon cancelling their resume filtering software with the excuse that it 'skewed male') is that 1. The AI system accurately predicted employee success across both genders AND 2. The AI system predicted that women would do worse than men That's politically embarrassing and something that you can't necessarily 'fix' by improving the system. (see: all the 'will this person commit a crime if let out on parole' systems that end up accurately discriminating based on race) This isn't to say that women are worse engineers than men, or anything of that sort - only that the applicant pool to Amazon was skewed, or women were treated worse in the workplace and thus performed worse, or a dozen other possible causes. (And only in this hypothetical scenario! I have no inside info from Amazon!)

gizmo686 7y ago
Your example is quite possible, particularly at an organazation that would be embarrased by such a result. Assume that the ability curve of male applicants and female applicants are identical; that the majority of applicants are male; and that Amazon wants to hire more females then would be expected given the portion of applicants that are female. A natural way of accomplishing this goal is to give extra points to female applicants [0]. Due to selection bias, the ability curve of women within the population of Amazon engineers would skew lower then men within the population of Amazon engineers. This is a special case of a more general phenomona. If you have signal S that is positivly correlated with a desired trait in the general population, and over select for S, you will find that S is negativly correlated within your population. [0]. All proposals I have seen amount to either a good approximation of this or changing the applicant pool. And, by assumption, the latter is excluded.
lotu 7y ago
This is feels like an elephant in the room when it comes to AI bias. We develop an AI that accurately predicts outcomes and discover it is biased, then instead of asking if maybe this means our current system is deeply biased and needs to be changed, we say, "don't use the AI; keep using the people who might or might not be biased but we don't know because we can't measure it in the way an AI can be measured." If it isn't acceptable to use an AI to create biased outcomes how is it acceptable to use people to create the the same outcomes. AI decision making can be examined and tuned in ways that people cannot.

turtlecloud 7y ago

Just remove the gender/sex as variables for the AI and maybe name too. Preprocess the resumes to remove them. Now you remove the majority of gender bias for the AI.

gizmo686 7y ago
AI is really good at infering information. If gender is a real signal, it would be very difficult to filter the input such that it is not making a determination by what could be reffered to inferred gender.

Bartweiss 7y ago

This story has been constantly misrepresented, because Reuters absolutely botched their initial report. Amazon was never building a tool to decide which interviewed candidates to hire, they were building a tool for discovering candidates. It was biased, but that gender bias wasn't the proximate reason for scrapping the tool. As far as I can tell from later stories (e.g. 1, 2), what Amazon actually did was build a tool to show recruiters 'quality' predictions for all resumes, for instance as they scrolled LinkedIn. But they trained it on resumes submitted to Amazon for various positions, possibly also adding weight to resumes which produced hires. In which case the problem is painfully obvious; the system effectively had no negative training data, and its positive examples (submitted resumes) didn't actually match the desired output (qualified resumes). It was computing degree of similarity between a gender-neutral-ish pool (resumes posted online) and a gender-skewed pool (resumes submitted to Amazon), and tried to make that conversion with whatever data was available - like devaluing resumes that mentioned women's colleges. (This wasn't just a proxy-variable thing, the model essentially learned to weight on gender.) Amazon's team apparently caught this issue and did the usual things like blinding on those words. But they were scared of uncaught factors; reading between the lines, they were unable to "detrain" biases like neural nets do because their dataset and task didn't match. Ultimately, the tool was apparently scrapped because it made selections "almost at random". Which, again, isn't exactly surprising in light of the absolutely bonkers choice of training examples. [1] https://www.aclu.org/blog/womens-rights/womens-rights-workplace/why-amazons-automated-hiring-tool-discriminated-against https://www.aclu.org/blog/womens-rights/womens-rights-workpl... [2] https://www.ml.cmu.edu/news/news-archive/2018/october/amazon-scraps-secret-artificial-intelligence-recruiting-engine-that-showed-biases-against-women.html https://www.ml.cmu.edu/news/news-archive/2018/october/amazon...

bumby 7y ago

Or couldn't they provide data augmentation on the same samples to give the effect of a more diverse (and more populous) training set? Using the blog's skin cancer example, couldn't the labelled images be augmented by altering the skin tones and adding these new examples to the training set? It seems to me that some of the anomalous results discussed in the article are actually the result of poor model design or poor pre-processing data choices. We can't just throw anything to any ol' machine learning model and expect it to be magic