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.

But the firm was ultimately forced to end the project after it found the system had taught itself to prefer male candidates over females.

Amazon was forced to shut down an experimental hiring tool after it was found to discriminate against female candidates. The tool used AI to give candidates scores ranging from one to five

HOW IT WORKED Amazon had been building computer programs since 2014 to review job applicants' resumes with the aim of mechanizing the search for top talent The experimental hiring tool used artificial intelligence to give job candidates scores ranging from one to five stars - much like shoppers rate products on Amazon. However, 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. Advertisement

The 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.

Automation has been key to Amazon's e-commerce dominance, be it inside warehouses or driving pricing decisions.

'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.

In effect, Amazon's system taught itself that male candidates were preferable. It penalized resumes that included the word 'women's,' as in 'women's chess club captain.'

And it downgraded graduates of two all-women's colleges, according to people familiar with the matter.

They did not specify the names of the schools.

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.

Amazon declined to comment on the recruiting engine, but says it is committed to workplace diversity and equality. The issue offers a case study into the limitations of machine learning

Amazon's recruiters looked at the recommendations generated by the tool when searching for new hires, but never relied solely on those rankings, they said.

Amazon declined to comment on the recruiting engine or its challenges, but the company says it is committed to workplace diversity and equality.

The company's experiment, which Reuters is first to report, offers a case study in the limitations of machine learning.

Some 55 percent of U.S. human resources managers said artificial intelligence, or AI, would be a regular part of their work within the next five years, according to a 2017 survey by talent software firm CareerBuilder.

Employers have long dreamed of harnessing technology to widen the hiring net and reduce reliance on subjective opinions of human recruiters.

But computer scientists such as Nihar Shah, who teaches machine learning at Carnegie Mellon University, say there is still much work to do.

'How to ensure that the algorithm is fair, how to make sure the algorithm is really interpretable and explainable - that's still quite far off,' he said.

Amazon's experiment began at a pivotal moment for the world's largest online retailer.

Machine learning was gaining traction in the technology world, thanks to a surge in low-cost computing power.

And Amazon's Human Resources department was about to embark on a hiring spree: Since June 2015, the company's global headcount has more than tripled to 575,700 workers, regulatory filings show.

AMAZON'S WAGES ROW Amazon has come under fire after it said it would scrap bonuses and stock handouts for its workers. The move came after Amazon said it would raise minimum wage for its workers from $10 per hour to $15. At the moment, workers are given a single Amazon share worth almost $2,000 when they join and an additional share for every year they work, The Guardian reports. If they hold on to the shares for two years, they can cash them in tax free. This scheme will be now phased out in favor of a sharesave scheme which will allow workers to buy shares at a discounted rate. Worker's unions said the average employee will have half of their pay rise wiped out by the changes, but some workers have said they will actually be worse off Workers who had been waiting for their 2020 or 2021 stocks to be unlocked will have to buy them. Meanwhile bonuses which allow staff to earn back 8 per cent of their salary per month - and 16 per cent in busy periods around Christmas - for hitting performance targets are also being scrapped. Many have taken to Twitter to express their fury over the decision. One user, going by the name Splitwig, wrote: 'I have many family members who work at Amazon and they lost a lot yesterday. 'They were already on $15 or above (most are at their facility), but lost all incentive and stock programs. They'll make $3-6k less per year now.' Advertisement

So it set up a team in Amazon's Edinburgh engineering hub that grew to around a dozen people.

Their goal was to develop AI that could rapidly crawl the web and spot candidates worth recruiting, the people familiar with the matter said. The group created 500 computer models focused on specific job functions and locations.

They taught each to recognize some 50,000 terms that showed up on past candidates' resumes.

The algorithms learned to assign little significance to skills that were common across IT applicants, such as the ability to write various computer codes, the people said.

Instead, the technology favored candidates who described themselves using verbs more commonly found on male engineers' resumes, such as 'executed' and 'captured,' one person said.

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.

Pictured is Amazon CEO Jeff Bezos. Amazon ultimately ended its AI job candidate system

With the technology returning results almost at random, Amazon shut down the project, they said.

Other companies are forging ahead, underscoring the eagerness of employers to harness AI for hiring.

Kevin Parker, chief executive of HireVue, a startup near Salt Lake City, said automation is helping firms look beyond the same recruiting networks upon which they have long relied.

His firm analyzes candidates' speech and facial expressions in video interviews to reduce reliance on resumes.

'You weren´t going back to the same old places; you weren´t going back to just Ivy League schools,' Parker said.

Microsoft's LinkedIn, the world's largest professional network, has gone further. It offers employers algorithmic rankings of candidates based on their fit for job postings on its site.

Still, John Jersin, vice president of LinkedIn Talent Solutions, said the service is not a replacement for traditional recruiters.

'I certainly would not trust any AI system today to make a hiring decision on its own,' he said. 'The technology is just not ready yet.'

Some activists say they are concerned about transparency in AI. The American Civil Liberties Union is currently challenging a law that allows criminal prosecution of researchers and journalists who test hiring websites' algorithms for discrimination.

HOW ARTIFICIAL INTELLIGENCES LEARN USING NEURAL NETWORKS AI systems rely on artificial neural networks (ANNs), which try to simulate the way the brain works in order to learn. ANNs can be trained to recognise patterns in information - including speech, text data, or visual images - and are the basis for a large number of the developments in AI over recent years. Conventional AI uses input to 'teach' an algorithm about a particular subject by feeding it massive amounts of information. AI systems rely on artificial neural networks (ANNs), which try to simulate the way the brain works in order to learn. ANNs can be trained to recognise patterns in information - including speech, text data, or visual images Practical applications include Google's language translation services, Facebook's facial recognition software and Snapchat's image altering live filters. The process of inputting this data can be extremely time consuming, and is limited to one type of knowledge. A new breed of ANNs called Adversarial Neural Networks pits the wits of two AI bots against each other, which allows them to learn from each other. This approach is designed to speed up the process of learning, as well as refining the output created by AI systems. Advertisement

'We are increasingly focusing on algorithmic fairness as an issue,' said Rachel Goodman, a staff attorney with the Racial Justice Program at the ACLU.

Still, Goodman and other critics of AI acknowledged it could be exceedingly difficult to sue an employer over automated hiring: Job candidates might never know it was being used.

As for Amazon, the company managed to salvage some of what it learned from its failed AI experiment.

It now uses a 'much-watered down version' of the recruiting engine to help with some rudimentary chores, including culling duplicate candidate profiles from databases, one of the people familiar with the project said.

Another said a new team in Edinburgh has been formed to give automated employment screening another try, this time with a focus on diversity.