Thanks to Amazon, the world has a nifty new cautionary tale about the perils of teaching computers to make human decisions. According to a Reuters report published Wednesday, the tech giant decided last year to abandon an “experimental hiring tool” that used artificial intelligence to rate job candidates, in part because it discriminated against women. Recruiters reportedly looked at the recommendations the program spat out while searching for talent, “but never relied solely on those rankings.” The misadventure began in 2014, when a group of Amazon engineers in Scotland set out to mechanize the company’s head-hunting process, by creating a program that would scour the Internet for worthwhile job candidates (and presumably save Amazon’s HR staff some soul crushing hours clicking around LinkedIn). “Everyone wanted this holy grail,” a source told Reuters. “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.” Advertisement Advertisement Advertisement It didn’t pan out that way. In 2015, the team realized that its creation was biased in favor of men when it came to hiring technical talent, like software developers. The problem was that they trained their machine learning algorithms to look for prospects by recognizing terms that had popped up on the resumes of past job applicants—and because of the tech world’s well-known gender imbalance, those past hopefuls tended to be men. “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,” Reuters reported. The program also decided that basic tech skills, like the ability to write code, which popped up on all sorts of resumes, weren’t all that important, but grew to like candidates who littered their resumes with macho verbs such as “executed” and “captured.” Advertisement After years of trying to fix the project, Amazon brass reportedly “lost hope“ and shuttered the effort in 2017. All of this is a remarkably clear-cut illustration of why many tech experts are worried that, rather than remove human biases from important decisions, artificial intelligence will simply automate them. An investigation by ProPublica, for instance, found that algorithms judges use in criminal sentencing may dole out harsher penalties to black defendants than white ones. Google Translate famously introduced gender biases into its translations. The issue is that these programs learn to spot patterns and make decisions by analyzing massive data sets, which themselves are often a reflection of social discrimination. Programmers can try to tweak the A.I. to avoid those undesirable results, but they may not think to, or be successful even if they try. Amazon deserves some credit for realizing its tool had a problem, trying to fix it, and eventually moving on (assuming it didn’t have a serious impact on the company’s recruiting over the last few years). But, at a time when lots of companies are embracing artificial intelligence for things like hiring, what happened at Amazon really highlights that using such technology without unintended consequences is hard. And if a company like Amazon can’t pull it off without problems, it’s difficult to imagine that less sophisticated companies can.<doc-sep>Amazon tried building an artificial-intelligence tool to help with recruiting, but it showed a bias against women, Reuters reports. Engineers reportedly found the AI was unfavorable toward female candidates because it had combed through male-dominated résumés to accrue its data. Amazon reportedly abandoned the project at the beginning of 2017. Amazon worked on building an artificial-intelligence tool to help with hiring, but the plans backfired when the company discovered the system discriminated against women, Reuters reports. Citing five sources, Reuters said Amazon set up an engineering team in Edinburgh, Scotland, in 2014 to find a way to automate its recruitment. The company created 500 computer models to trawl through past candidates' résumés and pick up on about 50,000 key terms. The system would crawl the web to recommend candidates. "They literally wanted it to be an engine where I'm going to give you 100 résumés, it will spit out the top five, and we'll hire those," one source told Reuters. A year later, however, the engineers reportedly noticed something troubling about their engine — it didn't like women. This was apparently because the AI combed through predominantly male résumés submitted to Amazon over a 10-year period to accrue data about whom to hire. Consequently, the AI concluded that men were preferable. It reportedly downgraded résumés containing the words "women's" and filtered out candidates who had attended two women-only colleges. Amazon's engineers apparently tweaked the system to remedy these particular forms of bias but couldn't be sure the AI wouldn't find new ways to unfairly discriminate against candidates. Gender bias was not the only problem, Reuters' sources said. The computer programs also spat out candidates who were unqualified for the position. Remedying algorithmic bias is a thorny issue, as algorithms can pick up on subconscious human bias. In 2016, ProPublica found that risk-assessment software used to forecast which criminals were most likely to reoffend exhibited racial bias against black people. Overreliance on AI for things like recruitment, credit-scoring, and parole judgments have also created issues in the past. Amazon reportedly abandoned the AI recruitment project by the beginning of last year after executives lost faith in it. Reuters' sources said Amazon recruiters looked at recommendations generated by the AI but never relied solely on its judgment. An Amazon spokesperson told Business Insider, "This was never used by Amazon recruiters to evaluate candidates."<doc-sep>SAN FRANCISCO (Reuters) - Amazon.com Inc's AMZN.O 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. 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’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 technology’s challenges, but said the tool “was never used by Amazon recruiters to evaluate candidates.” The company did not elaborate further. It did not dispute that recruiters looked at the recommendations generated by the recruiting engine. Slideshow ( 6 images ) The company's experiment, which Reuters is first to report, offers a case study in the limitations of machine learning. It also serves as a lesson to the growing list of large companies including Hilton Worldwide Holdings Inc HLT.N and Goldman Sachs Group Inc GS.N that are looking to automate portions of the hiring process. 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. MASCULINE LANGUAGE 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. 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. With the technology returning results almost at random, Amazon shut down the project, they said. Slideshow ( 6 images ) THE PROBLEM, OR THE CURE? 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. His company's customers include Unilever PLC ULVR.L and Hilton. Goldman Sachs has created its own resume analysis tool that tries to match candidates with the division where they would be the “best fit,” the company said. Microsoft Corp's MSFT.O 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.<doc-sep>Photo : Getty For years, a team at Amazon reportedly worked on software that vetted the resumes of job applicants in an effort to surface the most likely hires. It gradually became clear that no matter how hard engineers tried to fix it, the recruitment engine found a way to discriminate against women, Reuters reports. Advertisement On Wednesday, the outlet cited five sources familiar with the automated resume review program that began in 2014. According to those sources, a team that consisted of around a dozen engineers was tasked with building a program that would utilize machine learning to review a decade’s worth of resumes submitted to Amazon and its subsequent hiring practices. The goal was to teach an AI how to identify the most likely hires to streamline the list of potential recruits that would have to be subsequently vetted by human recruiters. From Reuters: 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. Gizmodo reached out to Amazon for comment on the report and a spokesperson sent us the following statement : “ This was never used by Amazon recruiters to evaluate candidates.” The algorithm’s gender discrimination issues became apparent about a year into the project’s lifecycle and it was eventually abandoned last year, the report said. It appears one of the primary issues was the dataset that Amazon had to work with. Most of the resumes submitted to the company over the previous decade came from men, and the tech sector has been controlled by men from its earliest days. Another issue cited in the report was the algorithm’s preference for language that was often used by male applicants. Common words and phrases like a proficiency in a certain programming language would be ignored and verbs like “executed” and “captured” were given more weight. After 500 iterations that were each trained to understand 50,000 unique terms, the team just couldn’t get the tool to stop reverting to discriminatory practices, Reuters reported. As time went on, the models often spiraled into recommending unqualified applicants at random. Advertisement The team’s effort highlights the limitations of algorithms as well as the difficulty of automating practices in a changing world. More women are joining the tech sector and all of the major tech giants have diversity initiatives in some form or another. But change has been painfully slow. Machines simply do what we tell them to do. If a machine is learning from example and we can only provide a sexist example, we’ll get sexist results. According to Reuters, a new team has been assembled at Amazon’s Edinburgh engineering hub to take another crack at the “holy grail” of hiring. Advertisement [Reuters]<doc-sep>
Amazon.com'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. 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'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. It also serves as a lesson to the growing list of large companies including Hilton Worldwide and Goldman Sachs that are looking to automate portions of the hiring process. 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.