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 ...
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!)
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.
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.
I don't think people are against using ML and for biased human systems. Just pointing out the ignorant, naive and lazy deference to computers that often occurs in human systems that share the same bias.
In short I'd think most people who are against biased AI are also against biased human systems for very similar reasons.
You can modify reality, but our understanding of biology - especially hormones - clearly tells us that the AI was right: men are generally better than women at weight lifting.
I'm not saying that every issue is like that, but it would be foolish to ignore that sometimes reality is biased, sometimes in obvious ways and sometimes more subtly.
Your post is great for the assumptions it encodes. Like what does it mean to be good at weight lifting? And that for some reason being good at weight lifting is a good proxy for being a good bouncer or construction worker?
For an off the cuff example it’s a great way to demonstrate the sort of bias we can naively introduce then defend because it’s just ‘reality’. When really it’s much more complex than identifying a relevant trait and assuming everything else falls out of it.
being a good weight lifter, means you can lift heavier weights then a less-good weight lifter. Whether this is a proxy for anything isn't relevant, because it's a purely contrived example. There are clearly jobs where physical strength (among other things) is important, and given the context of this example, there is no guarantee that a more complicated model evens out the differences.
The point of the example is, basically "there are some things which might discriminate strongly on the basis on physical traits, which might end up correlating with race/sex etc" - ask for a better model by all means, but there is no guarantee the perfect model will never correlate strongly with some political demographic, and hence be controversial.
"bias" and "reality" can equally cover for model simplicity.
> Gender bias was not the only issue. Problems with the data that underpinned the models’ judgments meant that unqualified candidates were often recommended for all manner of jobs, the people said. With the technology returning results almost at random, Amazon shut down the project, they said.
Apparently the recommendation system really did create gender bias, neither inherited from real differences nor from replicated human biases. (It looks like an issue with mismatched training data and task.) But that initial bias was found and corrected (2015) more than a year before the project was cancelled (2017) for providing "random" results. I think this is the most extreme case of algorithmic bias I've ever seen, but also the least commonly relevant; Amazon appears to have built a model which contained almost no rules except sexism, and scrapped it for not knowing anything worthwhile.
https://www.reuters.com/article/us-amazon-com-jobs-automatio...
The parole software was NOT being fed data for "will this person commit another crime". It was being fed data for, "will this person be a suspect for another crime".
The significant difference is that selective enforcement biases the data that it was trained on. Said selective enforcement has multiple causes, including the fact that heavier patrolling in black neighborhoods makes catching crimes more likely.
The size of the selective enforcement bias shows in a number of ways. For example consider drugs. In surveys, the usage of illegal drugs is the same in blacks and whites. And yet 6 times as many blacks are arrested for using illegal drugs as whites.
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.
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.
More sophisticated approaches are possible.
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.)
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...
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
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-workpl...
[2] https://www.ml.cmu.edu/news/news-archive/2018/october/amazon...