Therefore, to make this process better, employers have begun leveraging algorithmic techniques, in order to hire quality candidates. But the question that often comes up is if hiring algorithms prevent bias or amplify it? Algorithmic screening tools, on the surface seem like an appealing replacement for biased human evaluations. However, there experts have started realizing that these tools reproduce and sometimes magnify human biases found in the datasets based on which these tools are designed. Algorithms do not question the human decisions underlying a dataset – they attempt to replicate past decisions, and this can lead them to replicate all the human biases they were intended to remove in the first place.

In 2004, when a "webcam" was relatively unheard-of tech, Mark Newman knew that it would be the future of hiring. One of the first things the 20-year old did, after getting his degree in international business, was to co-found HireVue, a company offering a digital interviewing platform. While Newman lived at his parents' house, in Salt Lake City, the company, in its first five years, made just $100,000 in revenue. HireVue later received some outside capital, expanded and, in 2012, boasted some 200 clients--including Nike, Starbucks, and Walmart--which would pay HireVue, depending on project volume, between $5,000 and $1 million. Recently, HireVue, which was bought earlier this year by the Carlyle Group, has become the source of some alarm, or at least trepidation, for its foray into the application of artificial intelligence in the hiring process. No longer does the company merely offer clients an "asynchronous" interviewing service, a way for hiring managers to screen thousands of applicants quickly by reviewing their video interview --HireVue can now give companies the option of letting machine-learning algorithms choose the "best" candidates for them, based on, among other things, applicants' tone, facial expressions, and sentence construction.

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.

Machine learning is cresting the fresh wave of 2017 HR trends. Gartner research predicts algorithms will positively alter the behavior of over one billion global workers by 2020, while over 3 million people can look forward to "roboboss" supervisors. Yvonne Bauer, Head of Predictive Analytics at SAP SuccessFactors, sees machine learning becoming more widespread this year as part of HR's steady progression from art to data-driven science. "More companies will look into machine learning, moving from individual projects to actual products built into HCM suites," she said. "Conversational interfaces like chat bots and natural language processing will emerge this year, allowing companies to change how workers interact with the system and derive insights from those activities, including what people are working on and how engaged they are."

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