The Data Point Revolution: How Gig Platforms Are Changing the Way We Hire Professional Talent
Gig economy platforms like Uber and DoorDash have changed the way we get to the airport, order food, and find a place to stay when traveling. By now, you’ve probably heard the expression “Uber for X” to describe bringing these gig-platform models to other industries. Now, the model is changing how businesses hire workers, too.
Traditional hiring methods rely on resumes and word-of-mouth. As we all know, these processes are time-consuming and error-prone. As a result, it can take 30-45 days — and sometimes more — to hire a candidate. That’s far too long to be without the talent you need, especially if you’re looking for the flexibility and adaptability of a contract worker.
In contrast, gig platforms for recruiting and hiring allow an employer to find the best candidates fast, and it’s all based on data. The gig-platform model gives organizations access to a large pool of vetted professionals who can be available within hours, as opposed to weeks or months. Artificial intelligence (AI) and machine learning technologies, embedded into many gig platforms, can make the sourcing process exponentially faster, cheaper, and more accurate.
Gig platforms tend to work best when companies need to fill roles that are project-based and particularly expensive. Creative and technical workers such as software developers and designers have become the sweet spot for these platforms, but as the technology develops, we’re also seeing many other categories make the move to gig work. One such industry is the environmental, health, and safety (EHS) sector, which has long used consultants and freelance workers to fill short-term roles. As with creative and technical workers, EHS workers tend to operate on a per-project basis, and there are high costs to hiring these roles as full-time employees. Thus, gig platforms offer a less time-consuming way to find temporary, credentialed talent in these and similarly structured fields.
How Gig Platforms Use Artificial Intelligence
Gig-based hiring platforms start by building regional and national networks of workers. Unlike a traditional recruiting firm, these platforms are not tied to a specific location, nor do they have to wait until a contract is signed before they start searching for great talent. Gig platforms are continuously onboarding and vetting professionals to build deep rosters of qualified candidates. As job requests come in, they’re often run through AI-powered matching algorithms which surface suitable matches in mere moments.
On a gig platform, every single candidate data point can be used to refine a talent search, including details like education, skills, certifications, experience, industry, location, and availability. Machine learning models continually are trained based on this data, allowing them to make predictions about talent fit based on comparisons between training data and expected data. The more candidate data that is run through the platform, the better the models learn and perform.
Based on this description of the average gig platform’s inner workings, you might see why the Uber for X analogy is fair. However, the analogy does start to break down when you apply it to highly qualified and credentialed professionals. For example, the average EHS professional has at least a bachelor’s degree and 6-8 professional certifications, and the industry is highly regulated. It’s much easier to match riders with drivers than to sort through all the complexities of highly credentialed candidates.
But that’s precisely why robust data is the heart of the gig platform revolution in hiring. Before a quality match can be made, a platform must vet each professional and validate their accreditations. Without technology, this process is slow, time-consuming, and does not scale. With a gig platform, on the other hand, the skills and certifications of each professional are indexed and easily verified.
What About Bias?
There is one downside to relying on AI and machine learning models to make our matches: These algorithms can be biased. The data used to train matching algorithms may — unbeknownst to us — encode biases that cause the technology to reject or accept candidates based on irrelevant factors like gender, race, and income.
Luckily, there is a way to combat this problem. By excluding data points with known biases from training — and by regularly auditing the scoring system to ensure all biased data is truly excluded — we can train matching algorithms to rank candidates based on factors that truly matter.
The goal for any technology-based gig platform should be to level the playing field for candidates and employers alike by providing equal opportunities to all professionals and ensuring the best matches for hiring companies. Technology can sometimes exhibit biased behavior the same way recruiters and hiring managers can, but it’s much easier to remove that bias from a gig platform than to remove it from a human being.
Disrupting an industry is a long road, one that requires significant time, expertise, and capital. Progress is never easy, but it is necessary to grow the economy, create opportunities, and improve infrastructure for all. The gig economy has created the potential for large-scale, positive disruption in the world of recruiting and hiring. Data-driven platforms are leading the way.
Anthony Argenziano is the chief product officer for YellowBird.