Amazon Scraps Secret AI Recruiting Tool That Showed Bias Against Women (reuters.com)

Jeffrey Dastin, reporting for Reuters: Amazon's machine-learning specialists uncovered a big problem: their new recruiting engine did not like women. The team had been building computer programs since 2014 to review job applicants' resumes with the aim of mechanizing the search for top talent, five people familiar with the effort told Reuters. Automation has been key to Amazon's e-commerce dominance, be it inside warehouses or driving pricing decisions. The company's experimental hiring tool used artificial intelligence to give job candidates scores ranging from one to five stars -- much like shoppers rate products on Amazon, some of the people said. "Everyone wanted this holy grail," one of the people said. "They literally wanted it to be an engine where I'm going to give you 100 resumes, it will spit out the top five, and we'll hire those." But by 2015, the company realized its new system was not rating candidates for software developer jobs and other technical posts in a gender-neutral way. That is because Amazon's computer models were trained to vet applicants by observing patterns in resumes submitted to the company over a 10-year period. Most came from men, a reflection of male dominance across the tech industry.

[...] Amazon edited the programs to make them neutral to these particular terms. But that was no guarantee that the machines would not devise other ways of sorting candidates that could prove discriminatory, the people said. The Seattle company ultimately disbanded the team by the start of last year because executives lost hope for the project, according to the people, who spoke on condition of anonymity.

Comments

Re: Is this news?

[BlueStrat]

The real problem is the algorithm wasn't biased.

Ding ding ding! We have a winner!

Tell him what he's won, Johnny!

I had to scroll this far down to reach the actual answer?

Slashdot, I mourn for thee.

AC is exactly correct. The problem is not that the algorithm is racist or misogynist, it's that it's not biased at all while a large portion of our society *is* strongly biased and does not *want* unbiased answers that conflict with their current political/ideological/cultural/racial prejudices and stereotypes that are the tools essential to sustaining and growing group-identity politics and cultural Marxism which are all part of Post-Modernism which was created solely for the purpose of destroying post-Enlightenment Western civilization.

Strat

Re:Is this news?

[Cipheron]

Because the algorithm finds correlates to explain the data, and it always takes the "path of least resistance".

For example, say that women weren't hired as often because women tend to lack the necessary job experience more often than men. Then, the algorithm is looking for the *simplest possible* variable that correlates with that decision, so it notices otherwise irrelevant terms like "women's" or that successful candidates are more likely to use "masculine words". Those female candidates weren't rejected *because* of those word-selections, but because they tended to have less experience, but the algorithm isn't smart enough to understand that, so it glomps on b.s. word choices as a deciding factor.

For example, say that more successful candidates were more likely to wear good shoe brands, so your algorithm decides that choice of shoes should be a factor in hiring, because it's just that much easier for the algorithm to pick that signal out of the noise of the data you've fed into it. The original hirers didn't actually give a toss what shoes you wore, it's just that more successful people could afford better shoes to start with, but the algorithm hones in on "only hire nice-shoe-people" as if it's important. This is one of the problems with machine learning. It learns the "what" and not the "why" behind data correlations.