How to pronounce "misses"

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.

[The Way We Work]

Hiring as we know it is broken on many fronts.

It's a terrible experience for people.

About 75 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.

46 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 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.

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.

[Ready?]

[Begin!]

[Green circle]

[Green circle]

[Red circle]

[Green circle]

[Red circle]

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 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&P 500 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.