Hiring Algorithms Are Not Neutral

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More and more, human resources managers rely on data-driven algorithms to help with hiring decisions and to navigate a vast pool of potential job candidates. These software systems can in some cases be so efficient at screening resumes and evaluating personality tests that 72% of resumes are weeded out before a human ever sees them. But there are drawbacks to this level of efficiency. Man-made algorithms are fallible and may inadvertently reinforce discrimination in hiring practices. Any HR manager using such a system needs to be aware of its limitations and have a plan for dealing with them.


How artificial intelligence can eliminate bias in hiring

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Diversity (or lack of it) is still a major challenge for tech companies. Poised to revolutionize the world of work in general, some organizations are leveraging technology to root out bias, better identify and screen candidates and help close the diversity gap. That starts with understanding the nature of bias, and acknowledging that unconscious bias is a major problem, says Kevin Mulcahy, an analyst with Future Workplace and co-author of The Future Workplace Experience: 10 Rules for Managing Disruption in Recruiting and Engaging Employees. AI and machine learning can be an objective observer to screen for bias patterns, Mulcahy says. "The challenge with unconscious bias is that, by definition, it is unconscious, so it takes a third-party, such as AI, to recognize those occurrences and point out any perceived patterns of bias.


How artificial intelligence can eliminate bias in hiring

#artificialintelligence

Diversity (or lack of it) is still a major challenge for tech companies. Poised to revolutionize the world of work in general, some organizations are leveraging technology to root out bias, better identify and screen candidates and help close the diversity gap. That starts with understanding the nature of bias, and acknowledging that unconscious bias is a major problem, says Kevin Mulcahy, an analyst with Future Workplace and co-author of The Future Workplace Experience: 10 Rules for Managing Disruption in Recruiting and Engaging Employees. AI and machine learning can be an objective observer to screen for bias patterns, Mulcahy says. "The challenge with unconscious bias is that, by definition, it is unconscious, so it takes a third-party, such as AI, to recognize those occurrences and point out any perceived patterns of bias.


How artificial intelligence can eliminate bias in hiring

#artificialintelligence

Diversity (or lack of it) is still a major challenge for tech companies. Poised to revolutionize the world of work in general, some organizations are leveraging technology to root out bias, better identify and screen candidates and help close the diversity gap. That starts with understanding the nature of bias, and acknowledging that unconscious bias is a major problem, says Kevin Mulcahy, an analyst with Future Workplace and co-author of The Future Workplace Experience: 10 Rules for Managing Disruption in Recruiting and Engaging Employees. AI and machine learning can be an objective observer to screen for bias patterns, Mulcahy says. "The challenge with unconscious bias is that, by definition, it is unconscious, so it takes a third-party, such as AI, to recognize those occurrences and point out any perceived patterns of bias.


Will AI Remove Hiring Bias?

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If you've been following the latest hiring trends, you may have noticed that many recruiters are turning to artificial intelligence (AI) tools to tackle discrimination in hiring―and the expectations for success are high. However, HR technology analysts and even executives at companies offering AI solutions caution that a totally bias-free hiring process may be difficult to achieve. Amazon, the world's largest online retailer, found out the hard way. In 2015, the company discovered that a recruiting system it was building with machine-learning algorithms had begun to downgrade certain resumes that included words such as "women's club." By contrast, the system favored male candidates to whom such verbs such as "executed" and "captured" were attributed.