Algorithms up hiring process

46 percent of people get fired or as much as quit within the first year of starting their jobs. It’s pretty mind-blowing. It’s also bad for the economy. For the first time in history, we have more open jobs than we have unemployed people, and to me that screams that we have a problem.”

This opening by Priyanka Jain — The head of pymetrics at an NYC start-up has you hooked. Pymetrics uses neuroscience + AI to predict the right person for the job, while removing bias from the process and her talk focuses on how companies have been using new technology to find the right candidate for their jobs.

Speaking about the beginning of the problem of finding the right candidate for a position, Jain says that it all begins with the resume. “I believe that at the crux of all of this is a single piece of paper: the résumé. A résumé definitely has some useful pieces in it: what roles people have had, computer skills, what languages they speak, but what it misses is what they have the potential to do that they might not have had the opportunity to do in the past,” she says.

This is where technology is expected to step in. Algorithms have gotten pretty good at matching people to things, but what if same technology can help people find jobs that they are really well-suited for? But can we really depend on algorithms while job hunting?

To this, Jain says, “We can create algorithms that are more equitable and more fair than human beings have ever been. Every algorithm that we put into production has been pretested to ensure that it doesn’t favour any gender or ethnicity. And if there’s any population that’s being over favored, we can actually alter the algorithm until that’s no longer true. When we focus on the inherent characteristics that can make somebody a good fit for a job, we can transcend racism, classism, sexism, ageism -- even good schoolism.”