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Speaker 1: Welcome to tex Stuff, a production from I Heart Radio.

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Speaker 1: This season of Smart Talks with IBM is all about

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Speaker 1: new creators, the developers, data scientists, c t o s

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Speaker 1: and other visionaries creatively applying technology in business to drive change.

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Speaker 1: They use their knowledge and creativity to develop better ways

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Speaker 1: of working, no matter the industry. Join hosts from your

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Speaker 1: favorite Pushkin Industries podcasts as they use their expertise to

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Speaker 1: deepen these conversations, and of course Malcolm Gladwell will guide

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Speaker 1: you through the season as your host and provide his

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Speaker 1: thoughts and analysis along the way. Look out for new

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Speaker 1: episodes of Smart Talks with IBM on the I Heart

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Speaker 1: Radio app, Apple Podcasts, or wherever you get your podcasts,

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Speaker 1: and learn more at IBM dot com slash smart talks. Hello, Hello,

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Speaker 1: Welcome to Smart Talks with IBM, a podcast from Pushkin Industries,

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Speaker 1: I Heart Radio and ib AM. I'm Malcolm Gladmow. This season,

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Speaker 1: we're talking to new creators, the developers, data scientists, c

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Speaker 1: t o s and other visionaries who are creatively applying

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Speaker 1: technology in business to drive change. Channeling their knowledge and expertise,

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Speaker 1: they're developing more creative and effective solutions no matter the industry.

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Speaker 1: Our guest today is Angela Hood, founder and CEO of

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Speaker 1: This Way Global. Angela's mission is to eliminate discrimination in

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Speaker 1: the hiring process. Angela is a serial entrepreneur who saw

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Speaker 1: the potential to use automation technology as a way to

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Speaker 1: combat the human biases that lead to unfair hiring practices

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Speaker 1: and a less diverse, less competitive workforce. On today's show,

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Speaker 1: you'll hear how automation makes it easier than ever to

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Speaker 1: connect businesses with the right candidates, why automation is such

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Speaker 1: a powerful tool to mitigate bias, and how Angela's own

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Speaker 1: experiences with discriminatory hiring inspired her to take action. Angela

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Speaker 1: spoke with Jacob Goldstein, host of the pushkin podcast What's

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Speaker 1: Your Problem and former host of nprs Planet Money. Jacob

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Speaker 1: has been a business journalist for over a decade, reporting

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Speaker 1: for NPR, The Wall Street Journal, the Miami Herald, and

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Speaker 1: is the author of the book Money, The True Story

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Speaker 1: of a Made Up Thing. Okay, let's get to the interview.

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Speaker 1: Can you tell me just you know we're gonna get

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Speaker 1: into it a lot, but very briefly, what is this

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Speaker 1: weay Global? So our technology is built specifically to match

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Speaker 1: all people to all jobs without bias. And the last

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Speaker 1: part is the hardest part and also the most important.

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Speaker 1: And where did the idea for the company come from?

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Speaker 1: So I'm a female engineer, and um, you know, I'm

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Speaker 1: going out into the workforce after graduating, and I think

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Speaker 1: that just like everyone else, I can just put my

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Speaker 1: name up at the top of my resume and submit

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Speaker 1: this to companies and they'll know entertain me for an interview.

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Speaker 1: And what I found was because of the type of

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Speaker 1: engineering role I was looking for, which is in the

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Speaker 1: construction industry, that was not the case. The recruiters and

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Speaker 1: also the hiring managers at these companies would see my

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Speaker 1: name and think, well, I don't think that we want

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Speaker 1: a woman, or we don't think that she really understands

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Speaker 1: the job because it's out in the field. And so

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Speaker 1: a mentor of mine said, why don't you use your initials,

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Speaker 1: which are conveniently a L. And so I would submit

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Speaker 1: my resume as a L hood and people thought I

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Speaker 1: was a man. And so then at the same company,

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Speaker 1: for the same job, I would get interviews. And that

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Speaker 1: was the first moment where I realized there was a

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Speaker 1: lot of bias in the market and turns out that

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Speaker 1: there's a lot more biases, and we work to correct

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Speaker 1: for all of them. It's funny that kind of story

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Speaker 1: shouldn't be shocking, right, Like I know that I shouldn't

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Speaker 1: be shocked by it, and yet I still kind of am.

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Speaker 1: So clearly there's a tremendous amount of bias in the world,

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Speaker 1: and bias in recruiting. And you know, we're familiar with

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Speaker 1: these kind of stories of human bias, but but now

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Speaker 1: there's this new problem, right, which is algorithmic bias. What

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Speaker 1: is that tell me about algorithmic bias? A lot of

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Speaker 1: algorithms are underpinned by machine learning, and machine learning very

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Speaker 1: simply can happen kind of two different ways. You study

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Speaker 1: what has happened in the past, and you try to

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Speaker 1: duplicate that faster and more efficiently, and so that in

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Speaker 1: this context would be called supervised learning, and that seemed

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Speaker 1: like the logical place for nearly every recruiting technology to

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Speaker 1: start the problem with that is that there's been so

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Speaker 1: much historical bias that all you would really be doing

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Speaker 1: is capturing that company or that hiring manager recruiters bias

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Speaker 1: and duplicating it really fast, very efficiently. So you would

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Speaker 1: just be expanding bias much much faster than how a

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Speaker 1: human could. The flip side is something that's called unsupervised.

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Speaker 1: So this is where you build a system essentially a

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Speaker 1: black box. It's doing all types of calculations and decisions internally,

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Speaker 1: and then it's not biased in theory, but you have

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Speaker 1: no idea what it's basing its opinions on, so it

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Speaker 1: can kind of create bizarre results. Also, it's not explainable,

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Speaker 1: and so then you get caught in this catch twenty

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Speaker 1: two of I don't want to do bias at scale,

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Speaker 1: but I need to be explainable. So what do you do?

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Speaker 1: After thirteen failures, we finally figured out a way to

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Speaker 1: do this. The methodology that we finally found that innerrated

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Speaker 1: the results that run biased was not ever allowing the

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Speaker 1: math model or the computer to see the information that

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Speaker 1: causes bias. So we had to not let gender enter

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Speaker 1: into the system, ethnicity couldnot enter into the system, things

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Speaker 1: like that, and so then the logical question is, okay, well,

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Speaker 1: so if you don't allow those pieces of information to

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Speaker 1: come in, how can you then enable qualified people that

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Speaker 1: are also diverse to surface without bias? And the answer

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Speaker 1: is when you remove these factors. It happens naturally, and

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Speaker 1: we learned this by testing. We've had fifteen and a

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Speaker 1: half trillion matching events go through our system and almost

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Speaker 1: it's been now almost a decade, and through this we've

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Speaker 1: learned a lot. People are very diverse. If you will

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Speaker 1: just remove your own bias, you'll start seeing them. So

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Speaker 1: it's a it's an automated version of what you as

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Speaker 1: an individual did, uh before you started the company, when

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Speaker 1: you switch from putting your full name on your applications

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Speaker 1: to just your initials, effectively hiding your gender. Yeah, it

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Speaker 1: started with that. What we also learned though, is even

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Speaker 1: if you conceal your name, there are words where maybe

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Speaker 1: someone is a waitress in a previous job, and so

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Speaker 1: then the persons like, ah, that's a female, right, So

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Speaker 1: then we had to go one step further. We had

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Speaker 1: to say, now we have to neutralize these gender specific

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Speaker 1: words inside resume so that a person cannot look at

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Speaker 1: the document and still sus out ethnicity, gender and other

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Speaker 1: biasing attributes. It's remarkable that after hiding prejudicial information from

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Speaker 1: the computer like a candidate's gender or ethnicity, a qualified

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Speaker 1: diverse workforce assembled naturally as a result, for the overburdened recruiter.

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Speaker 1: That means there are huge advantage is to using intelligent automation.

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Speaker 1: That's a win win. As the conversation continues, Angela explains

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Speaker 1: how IBMS technology enabled her to simplify her customers hiring processes,

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Speaker 1: and she also shed some light on how far intelligent

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Speaker 1: automation has come in the past few years. How does

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Speaker 1: intelligent automation look different today than it did, say, five

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Speaker 1: years ago, it actually works, as the first thing. The

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Speaker 1: the level of innovation that has taken place is absolutely incredible.

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Speaker 1: And here's the thing about it is people have had

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Speaker 1: some negative interactions with things that said that they were

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Speaker 1: automated and they're now they're like, I don't want to

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Speaker 1: use it. The level of innovation that has happened is

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Speaker 1: absolutely incredible. And for them to not try something because

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Speaker 1: they tried something a decade ago and it didn't work,

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Speaker 1: that it's just completely the wrong approach. We're going to

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Speaker 1: see massive innovation over the next five to ten years too,

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Speaker 1: and you don't want to miss that. You don't want

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Speaker 1: to say, oh, I said on the sidelines because I

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Speaker 1: had a bad experience a decade ago. So I think

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Speaker 1: if you know, if you're anywhere involved in technology or

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Speaker 1: business growth, you need to be part of this. This

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Speaker 1: is your economy in play a role. So what is

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Speaker 1: a digital employee? Right? So our partnership with IBM, Watson

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Speaker 1: Orchestrate is around the DIG. So D I, G. E.

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Speaker 1: Y is a DIG who's a digital employee And I

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Speaker 1: always think of it honestly, is more of a concierge.

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Speaker 1: You can have all of your job descriptions living inside

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Speaker 1: of Box, for instance, and so there's all the job

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Speaker 1: descriptions and you're like, oh, I need to find someone

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Speaker 1: for this job. Watson goes into Box, grabs the odd

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Speaker 1: description and then sends that into this way system and

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Speaker 1: this way automatically surfaces up to three hundred qualified people

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Speaker 1: from diverse organizations. Right, so now the recruiter has not

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Speaker 1: had to figure out where are they going to source

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Speaker 1: these people from. They haven't had to sort out how

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Speaker 1: they're going to reach out to diverse organizations because we

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Speaker 1: have partners. And so now that part has been taken

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Speaker 1: care of, and then Wat's an organestrate does the next step,

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Speaker 1: which is sends out communication to the candidates that you

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Speaker 1: are interested in automatically, and then you get to sit

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Speaker 1: and wait for these people to respond back to you

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Speaker 1: of their interest in discussing something with you. Now all

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Speaker 1: of this has been automated, and essentially what I just

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Speaker 1: described could easily take a person three weeks to go

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Speaker 1: and identify all the talent. So you take three weeks

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Speaker 1: and you put this down to roughly three or four minutes.

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Speaker 1: Now it's absolutely incredible, and I think it gives recruiters

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Speaker 1: the time to do what they really want to do,

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Speaker 1: which is talked to people. How did you decide that

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Speaker 1: automation was the right tool to fight bias? That was

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Speaker 1: a journey, be as I think a lot of entrepreneurship

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Speaker 1: is an innovation. When we hire technology, we're hiring technology

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Speaker 1: to do a job for us. So what is the

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Speaker 1: job to be done here? It is to identify qualified

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Speaker 1: talent without bias. So when you start breaking this down,

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Speaker 1: you realize that if humans could do it, we would

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Speaker 1: have already done it. There's been a desire to have

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Speaker 1: this happen for many, many years, and we were not

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Speaker 1: successful at it. And the reason why is bias is

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Speaker 1: not discrimination. These things get confused all the time. Bias

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Speaker 1: is a product of our survival mechanism. We are always

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Speaker 1: going to survive as humans, and so we we need

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Speaker 1: these survival skills. That's part of bias. So we're not

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Speaker 1: going to get rid of it, and it's not a

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Speaker 1: character flaw. Bias is just inherently human and we are human.

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Speaker 1: And the best purpose that I think technology can serve

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Speaker 1: is the fact that it can do some things that

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Speaker 1: we can't do. We have to be very careful about

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Speaker 1: how we engineer it. Our own technology was engineered with

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Speaker 1: removing bias is the priority. But we can really have

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Speaker 1: technology make us better humans because it can do things

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Speaker 1: we can't do. Despite the potential to vastly improve the

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Speaker 1: way we hire, most companies still think automation is inaccessible,

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Speaker 1: perhaps a luxury to aspire to in the future, but

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Speaker 1: we live in a time when companies are hungrier than

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Speaker 1: ever to fill positions quickly. Jacob asked Angela what automation

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Speaker 1: can deliver for businesses today and how a companies creative

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Speaker 1: It is linked with its diversity. How prevalent is intelligent

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Speaker 1: automation in talent acquisition workflows today? So our data says

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Speaker 1: that in enterprise that roughly seven percent have adopted some

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Speaker 1: level of truly automated technology. But when you look at

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Speaker 1: the job market in toll like, you know, if you

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Speaker 1: look at the millions of employers we have, it's you know,

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Speaker 1: less than three percent have adopted automation. These are companies

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Speaker 1: that have a smaller workforce to do a great amount

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Speaker 1: of work. They're recovering from a pandemic, they need help,

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Speaker 1: and they think that automation is expensive, and it's actually

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Speaker 1: the opposite. It's not expensive at all. And so I

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Speaker 1: would encourage businesses that are mid market in small businesses

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Speaker 1: to embrace technology in a way that they haven't done. So,

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Speaker 1: I mean, there's one more piece of sort of what's

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Speaker 1: going on now that seems really interesting in the context

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Speaker 1: of what you do, and that is the incredible demand

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Speaker 1: for workers right now. Right there's I don't know, ten

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Speaker 1: million plus job openings, there's the great resignation, and so

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Speaker 1: I'm curious how automation is helping both companies and workers

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Speaker 1: through this process. Now, there's never been a job market

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Speaker 1: have to think of as employers, we have to think

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Speaker 1: every one person looking for a job. Okay, so that

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Speaker 1: is astounding to begin with. But of the jobs that

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Speaker 1: So now you have to go out and you need

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Speaker 1: cultivating a relationship with the people that do have the

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Speaker 1: skills you need. When you go to them, you need

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Speaker 1: you to come to work for us. That's number one.

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Speaker 1: Two you need to say. And when you get here,

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Speaker 1: we're going to help you automate those parts of your

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Speaker 1: job that you've never really enjoyed before, because we want

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Speaker 1: you win talent in this market. Yeah, what have you

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Speaker 1: heard back from recruiters about about this? You know, increased

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Speaker 1: integration of technology. So one of the things that I

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Speaker 1: think has been maybe the most surprising is that it's

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Speaker 1: really opened up the communication between hiring managers and recruiters

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Speaker 1: silo of hiring managers putting out job descriptions and saying recruiters,

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Speaker 1: the candidates or there's some questions around what are the

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Speaker 1: real job specific requirements, and they have trouble getting those

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Speaker 1: this more efficient, you start getting much better interactions between

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Speaker 1: the entire company. And in this current market, companies are

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Speaker 1: truly desperate to find the talent that they need. The

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Speaker 1: Let's talk about the diversity piece sort of you know,

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Speaker 1: landing here right. So on the diversity side, how does

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Speaker 1: how does a diverse workforce help make a business more creative?

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Speaker 1: A lot of the big consulting firms have dug in

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Speaker 1: R O I around diversity, And uniformly the answer has

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Speaker 1: been yes. There is increased profits, a more consistent workforce,

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Speaker 1: meaning people don't want to leave. There's not the same

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Speaker 1: level of attrition when the workforce is more diverse, and

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Speaker 1: better recruiting numbers. So all of that is like the outcome.

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Speaker 1: But I think the key thing to understand is the

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Speaker 1: why behind this. The why is that when you're diverse,

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Speaker 1: you come to solutions and you come to questions and

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Speaker 1: challenges from a different perspective. And when you have a

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Speaker 1: the market and you are using their insight to develop

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Speaker 1: better solutions, You're going to create better solutions. You're going

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Speaker 1: to get those solutions to market faster. You're going to

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Speaker 1: understand positioning of your value proposition inside the market. All

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Speaker 1: of these things happened with far more clarity when you

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Speaker 1: have a diverse workforce. You mentioned earlier that you failed

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Speaker 1: was it thirteen times? And I'm curious if sort of

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Speaker 1: getting through those failures and working your way to success

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Speaker 1: I would say that would be an understatement at moments. Uh,

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Speaker 1: there are times where you know, I just say, like

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Speaker 1: thirteen failures kind of in passing. But there were times

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Speaker 1: where I felt like I was close to breaking as

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Speaker 1: an innovator, and the fact that was, like, there's just

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Speaker 1: non solution for this. The thirteen failures is incredibly gut wrenching.

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Speaker 1: But I was fortunate. I had very supportive and susters

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Speaker 1: and so we got through it. Uh, And I'm very

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Speaker 1: proud of the company we are today because of those failures.

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Speaker 1: So just to to wrap up, let's let's talk a

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Speaker 1: little bit about the future. We've done the past, we've

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Speaker 1: done the present. Let's talk a little bit about the future.

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Speaker 1: I mean, how do you think the hiring process will

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Speaker 1: look in the future, whatever, five years, ten years, And

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Speaker 1: in particular, what role will will automation, intelligent automation, augmented intelligence,

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Speaker 1: what role will will all that play? Well, if you

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Speaker 1: look back in decades ago, there were people that would

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Speaker 1: work for the same company for ten twenty years, and

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Speaker 1: that was, you know, not that unusual. Now very uncommon,

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Speaker 1: and in the future, I think it will be absolutely rare.

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Speaker 1: I think we're looking more likely at people that will

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Speaker 1: work for multiple companies. We're seeing that with the rise

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Speaker 1: of the gig economy, we obviously are seeing people love

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Speaker 1: to work remote. I know when we have an active

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Speaker 1: job that goes out into our marketplace and if it

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Speaker 1: is remote and also prioritize diversity, you will have twenty

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Speaker 1: to thirty times more applicants. So I think that we're

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Speaker 1: going to start seeing companies really investing in those two attributes,

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Speaker 1: trying to keep as many jobs remote as possible, just

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Speaker 1: because it attracts talent that companies are really struggling to

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Speaker 1: find right now. And I think the level of automation

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Speaker 1: is going to continue to increase, that will continue to

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Speaker 1: increase an investment over the next five to ten years.

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Speaker 1: In twenty years, I think we will all look back

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Speaker 1: and say, why did we all do these crazy parts

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Speaker 1: of our job? Why didn't we automate those It's because

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Speaker 1: we were waiting for technology like Orchestrate provides. Do you

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Speaker 1: have any specific advice for businesses that want to incorporate

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Speaker 1: technology and automation in their in their business and their work.

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Speaker 1: I would say, realize that you use automation every day,

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Speaker 1: You use AI every day. So when you're using Google

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Speaker 1: Maps or something like that, you're you're using your smartphone.

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Speaker 1: You're accessing this kind of technology as a consumer, as

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Speaker 1: an individual, there's no reason why you should worry about

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Speaker 1: adopting it as a business, and don't feel intimidated by it.

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Speaker 1: You are absolutely ready to use it and your business

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Speaker 1: is ready to benefit from it. Just don't have that fear.

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Speaker 1: We certainly is a company work with companies of all sizes.

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Speaker 1: We have companies that have five to tend employees only,

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Speaker 1: and we have some that have hundreds of thousands employees.

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Speaker 1: That's a great thing about automation is it doesn't care

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Speaker 1: the size of your company. It will work for you.

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Speaker 1: Angela's fun to talk to you. Thank you for your time,

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Speaker 1: congratulations and making it through to thirty And if you

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Speaker 1: really think about that, that's a It's a really impressive

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Speaker 1: level of persistence. Like I could imagine failing a few times,

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Speaker 1: but I would have given up at nine or something

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Speaker 1: at seven. At seven, I was like, I'm a crazy person.

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Speaker 1: It is vitally important to get hiring right. What could

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Speaker 1: be more essential to an organization's success than deciding which

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Speaker 1: human beings make up that organization. If we let our

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Speaker 1: biases go unchecked, we end up excluding qualified candidates, leaving

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Speaker 1: our workforce is less diverse and therefore less competitive because

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Speaker 1: of it. Angela made an interesting point earlier that I

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Speaker 1: want to go back to. She said that bias is

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Speaker 1: not a character flaw, it's a survival instinct, and that

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Speaker 1: the best purpose technology can serve is to make us

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Speaker 1: better humans by doing things for us that we can't.

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Speaker 1: Bias is in human nature and we'll never truly get

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Speaker 1: rid of it, but the first step to minimizing its

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Speaker 1: impact is to acknowledge it's a problem we need help with.

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Speaker 1: Intelligent automation can make hiring more efficient. When we allow

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Speaker 1: computers to mitigate our biases, better hiring is the result.

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Speaker 1: Sometimes to build the best team possible, we have to

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Speaker 1: know when to listen to our human instincts and when

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Speaker 1: to set them aside. On the next episode of Smart

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Speaker 1: Talks with IBM, how to use data creatively in order

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Speaker 1: to solve novel problems, We talk with YouTube content creator

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Speaker 1: and IBM's senior Data science and AI technical specialist, Nicholas Renaud.

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Speaker 1: Smart Talks with IBM is produced by Matt Romano, David

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Speaker 1: jaw Royston Deserve and Edith Rousselo with Jacob Goldstein were

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Speaker 1: edited by Sophie Crane. Are Engineers are Jason Gambrel, Sarah

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Speaker 1: Bragair and Ben Tolliday. Theme song by Granmascope. Special thanks

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Speaker 1: to Carlie mcglory, Andy Kelly, Kathy Callaghan and the eight

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Speaker 1: Bar and IBM teams, as well as the Pushkin marketing team.

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Speaker 1: Smart Talks with IBM is a production of Pushkin Industries

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