Businesses in nearly every industry have come to rely on contingent talent as specialists and flexible experts rather than temps who fill vacant seats during absences, seasonal demands or personnel transitions. With increasing frequency, we’re integrating these skilled contractors into our primary workforces. Yet, we still haven’t integrated them into our internal knowledge systems. And that’s a missed opportunity to tap into their intelligence and ideas, especially as the sharing economy’s[...]
Full disclosure: I hate Smash Mouth. And I share this opinion freely online. But here’s where the irony of life seeps into the digital world. Google’s algorithms, astonishing as they are in countless respects, ostensibly deem me one of the band’s biggest fans -- merely as a result of posting critiques about them or searching for unflattering parodies of “All Star.” It’s a problem with content curation and the individualization of information. Searching for something shouldn’t presume adoration; but the Internet hasn’t quite mastered that lesson yet. And in the staffing industry, where “talent curation” has become one of the latest catch phrases, it’s critical that we understand the proper application and practice so that we’re curating talent, not biases.
The global dialog concerning the future of artificial intelligence (AI), at least in the media, appears to have become very binary. On one side of the debate, technophiles excitedly praise the coming singularity, when machines and humans will merge. Across the aisle, visionaries like Elon Musk are portrayed as neo-Luddites, prophets of doom offering grim auguries of a robot-spawned dystopia. The reality, as always, lies somewhere in the center of the otherwise sensationalized argument. And I believe both sides are in agreement. Yes, AI promises a wealth of advances that can better society -- and the people who work in it. However, it can also deliver some pretty disastrous results, as we’ve seen already. The solution comes down to this: AI will learn what we humans teach it. The best way to ensure our mutual success is to approach machine learning as we should hiring: by eliminating bias from the process.
People love a good underdog story -- one about overcoming adversity, rising up from humble beginnings to achieve great things, and conquering stereotypes or misperceptions. It’s the stuff we’re raised on in the land of opportunity. The fables and fairy tales we first experience assure us that we can succeed regardless of our stations in life. These Cinderella stories inspire and motivate us. They’ve even infused themselves, subtly, into the fabric of staffing. Recruiters are now encouraged to find rockstar talent based on values, fit, skills, creativity -- not Ivy League degrees, positions at prestigious firms or years of service. And yet when it comes to many hiring managers, this is where the story ends up a piece of fiction. They fall back on the familiar habits of looking for academic credentials, keywords and specific work experience. Yes, the same folks who may have shed a tear at Cinderella’s triumph could be more likely to hire one of her stepsisters, based on a resume. We’re not going to win the talent wars if we accept only seasoned officers from the equivalents of Annapolis or West Point. It’s time to reconsider the concept of experience.