Oct 11

Implicit/Machine learning gender bias

I ran across a headline recently “Amazon scraps secret AI recruiting tool that showed bias against women” that I realized provides a nice example of a few points we’ve been discussing in the lab.

First, I have found myself describing on a few recent occasions that it is reasonable to think of implicit learning (IL) as the brain’s machine learning (ML) algorithm.  ML is a super-hot topic in AI and data science research, so this might be a useful analogy to help people understand what we mean by studying IL.  We characterize IL as the statistical extraction of patterns in the environment and the shaping of cognitive processing to maximize efficiency and effectiveness to these patterns.  And that’s the form of most of the really cool ML results popping up all over the place.  Automatic statistical extraction from large datasets that provides better prediction or performance than qualitative analysis had done.

Of course, we don’t know the form of the brain’s version of ML (there are a lot of different computational variations of ML) and we’re certainly bringing less computational power to our cognitive processing problems than a vast array of Google Tensor computing nodes.  But perhaps the analogy helps frame the important questions.

As for Amazon, the gender bias result they obtained is completely unsurprising once you realize how they approached the problem.  They trained an ML algorithm on previously successful job applicants to try to predict which new applicants would be most successful.  However, since the industry and training data was largely male-dominated, the ML algorithm picked up this as a predictor of success.  ML algorithms make no effort to distinguish correlation from causality, so they will generally be extremely vulnerable to bias.

But people are also vulnerable to bias, probably by basically the same mechanism.  If you work in a tech industry that is male dominated, IL will cause you to nonconsciously acquire a tendency to see successful people as more likely to be male.  Then male job applicants will look closer to that category and you’ll end up with an intuitive hunch that men are more likely to be successful — without knowing you are doing it or intending any bias at all against women.

An important consequence of this is that people exhibiting bias are not intentionally misogynistic (also note that women are vulnerable to the same bias).  Another is that there’s no simple cognitive control mechanism to make this go away.  People rely on their intuition and gut instincts and you can’t simply tell people not to as not doing so feels uncomfortable and unfamiliar.  The only obvious solution to this is a systematic, intentional approach to changing the statistics by things like affirmative action.  A diverse environment will eventually break your IL-learned bias (how long this takes and what might accelerate it is where we should be looking to science), but it will never happen overnight and will be an ongoing process that is highly likely to be uncomfortable at first.

In theory, it should be a lot quicker to fix the ML approach.  You ought to be able to re-run the ML algorithm on a non-biased dataset that equally successful numbers of men and women.  I’m sure the Amazon engineers know that but the fact that they abandoned the project instead suggests that the dataset must have been really biased initially.  You need a lot of data for ML and if you restrict the input to double the size of the number of successful women, you won’t have enough data if the hiring process was biased in the past (prior bias would also be a likely reason you’d want to tackle the issue with AI).  They’d need to hire a whole lot more women — both successful and unsuccessful, btw, for the ML to work — and then retrain the algorithm.  But we knew that was the way out of bias before we even had ML algorithms to rediscover it.

Original article via Reuters: https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G

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