Deep Listening

Deep Listening

Listen again to the video several times, trying to understand every word. After listening several times, read through the transcript below to identify any language that you were unable to understand. Make notes about any language that you find particularly useful or interesting. Use a dictionary to find definitions and additional usage examples.

Transcript

Applying for jobs online is one of the worst digital experiences of our time. And applying for jobs in person really isn’t much better.

Hiring as we know it is broken on many fronts. It’s a terrible experience for people. About seventy-five percent of people who applied to jobs using various methods in the past year said they never heard anything back from the employer. And at the company level it’s not much better. Forty-six percent of people get fired or 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. 

I believe that at the crux of all of this is a single piece of paper: the resume. A resume 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. And with such a quickly changing economy where jobs are coming online that might require skills that nobody has, if we only look at what someone has done in the past, we’re not going to be able to match people to the jobs of the future. 

So this is where I think technology can be really helpful. You’ve probably seen that algorithms have gotten pretty good at matching people to things, but what if we could use that same technology to actually help us find jobs that we’re really well-suited for? But I know what you’re thinking. Algorithms picking your next job sounds a little bit scary, but there is one thing that has been shown to be really predictive of someone’s future success in a job, and that’s what’s called a multimeasure test. Multimeasure tests really aren’t anything new, but they used to be really expensive and required a PhD sitting across from you and answering lots of questions and writing reports. 

Multimeasure tests are a way to understand someone’s inherent traits – your memory, your attentiveness. What if we could take multimeasure tests and make them scalable and accessible, and provide data to employers about really what the traits are of someone who can make them a good fit for a job? 

This all sounds abstract. Let’s try one of the games together. You’re about to see a flashing circle, and your job is going to be to clap when the circle is red and do nothing when it’s green. 

Maybe you’re the type of person who claps the millisecond after a red circle appears. Or maybe you’re the type of person who takes just a little bit longer to be 100 percent sure. Or maybe you clap on green even though you’re not supposed to. The cool thing here is that this isn’t like a standardized test where some people are employable, and some people aren’t. Instead it’s about understanding the fit between your characteristics and what would make you good at a certain job. We found that if you clap late on red and you never clap on the green, you might be high in attentiveness and high in restraint. People in that quadrant tend to be great students, great test-takers, great at project management or accounting. But if you clap immediately on red and sometimes clap on green, that might mean that you’re more impulsive and creative, and we’ve found that top-performing salespeople often embody these traits. 

The way we actually use this in hiring is we have top performers in a role go through neuroscience exercises like this one. Then we develop an algorithm that understands what makes those top performers unique. And then when people apply to the job, we’re able to surface the candidates who might be best suited for that job. 

So you might be thinking there’s a danger in this. The work world today is not the most diverse and if we’re building algorithms based on current top performers, how do we make sure that we’re not just perpetuating the biases that already exist? For example, if we were building an algorithm based on top performing CEOs and use the S&P500 as a training set, you would actually find that you’re more likely to hire a white man named John than any woman. And that’s the reality of who’s in those roles right now. But technology actually poses a really interesting opportunity. 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 favor any gender or ethnicity. And if there’s any population that’s being overfavored, 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. 

Our best technology and algorithms shouldn’t just be used for helping us find our next movie binge or new favorite Justin Bieber song. Imagine if we could harness the power of technology to get real guidance on what we should be doing based on who we are at a deeper level.