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Speaker 1: Hey everyone, it's Robert and Joe here. Today we've got

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Speaker 1: something a little bit different to share with you. It

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Speaker 1: is a new edition of the Smart Talks podcast series,

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Speaker 1: which is produced in partnership with IBM. This season of

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Speaker 1: Smart Talks with IBM is all about new creators, the developers,

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Speaker 1: data scientists, c t o s, and other visionaries creatively

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Speaker 1: applying technology and business to drive change. They use their

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Speaker 1: knowledge and creativity to develop better ways of working, no

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Speaker 1: matter the industry. Join hosts from your favorite Pushkin Industries

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Speaker 1: podcast as they use their expertise to deepen these conversations.

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Speaker 1: Malcolm Gladwell will guide you through this season as your

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Speaker 1: host to provide his thoughts and analysis along the way.

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Speaker 1: Look out for new episodes of Smart Talks with IBM

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Speaker 1: every month on the I Heart Radio app, Apple Podcasts,

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Speaker 1: or wherever you get your podcasts. And learn more at

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Speaker 1: IBM dot com slash smart Talks. Hello, Hello, Welcome to

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

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Speaker 1: Heart Radio and IBM. I'm Malcolm Gladwell. This season we're

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Speaker 1: talking to new creators, the developers, data scientists, ct os,

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Speaker 1: and other visionaries who are creatively applying technology in business

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Speaker 1: to drive change. Channeling their knowledge and expertise, they're developing

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Speaker 1: more creative and effective solutions, no matter the industry. Our

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

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

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

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

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

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

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Speaker 1: hear how automation makes it easier than ever to connect

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

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

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

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

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

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

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

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

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

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

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Speaker 1: it a lot, but very briefly, what is this way global?

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Speaker 1: So our technology is built specifically to match all people

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

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Speaker 1: the hardest part and also the most important. And where

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Speaker 1: did the idea for the company come from? So I'm

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

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Speaker 1: into the workforce after graduating, and I think that just

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

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Speaker 1: at the top of my resume and submit this to

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

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

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

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Speaker 1: that was not the case. The recruiters and also the

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Speaker 1: hiring managers at these companies would see my name and think, well,

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Speaker 1: I don't think that we want a woman, or we

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Speaker 1: don't think that she really understands the job because it's

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Speaker 1: out in the field. And so a mentor of mine said,

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Speaker 1: why don't you use your initials, which are conveniently a L.

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Speaker 1: And so I would submit my resume as a L

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Speaker 1: hood and people thought I was a man. And so

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Speaker 1: then at the same company, for the same job, I

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Speaker 1: would get interviews, and that was the first moment where

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Speaker 1: I realized there was a lot of bias in the market.

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Speaker 1: Turns out that there's a lot more biases, and we

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Speaker 1: work to correct for all of them. It's funny that

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Speaker 1: kind of story shouldn't be shocking, right, Like I know

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Speaker 1: that I shouldn't be shocked by it, and yet I

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Speaker 1: still kind of am. So clearly there's a tremendous amount

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Speaker 1: of bias in the world, and bias in recruiting. And

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Speaker 1: you know, we're familiar with these kind of stories of

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Speaker 1: human bias, but but now there's this new problem, right,

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Speaker 1: which is algorithmic bias. What is that tell me about

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Speaker 1: algorithmic bias? A lot of algorithms are underpinned by machine learning,

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Speaker 1: and machine learning very simply can happen kind of two

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Speaker 1: different ways. You study what has happened in the past,

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Speaker 1: and you try to duplicate that faster and more efficiently,

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Speaker 1: and so that in this context would be called supervised learning.

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Speaker 1: And that seemed like the logical place for nearly every

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Speaker 1: recruiting technology to start the problem with that is that

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Speaker 1: there's been so much historical bias that all you would

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Speaker 1: really be doing is capturing that company or that hiring

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Speaker 1: manager recruiters bias and duplicating it really fast, very efficiently,

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Speaker 1: So you would just be expanding bias much much faster

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Speaker 1: than a human could. The flip side is something that's

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Speaker 1: called unsupervised. So this is where you build a system

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Speaker 1: essentially a black box. It's doing all types of calculations

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Speaker 1: and decisions internally, and then it's not biased in theory,

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Speaker 1: but you have no idea what it's basing its opinions on,

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Speaker 1: so it can kind of create bizarre results. Also, it's

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Speaker 1: not explainable, and so then you get caught in this

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Speaker 1: catch twenty two of I don't want to do bias

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Speaker 1: at scale, but I need to be explainable. So what

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Speaker 1: do you do? After thirteen failures, we finally figured out

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Speaker 1: a way to do this. The methodology that we finally

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Speaker 1: found that generated the results that runbiased was not ever

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Speaker 1: allowing the math model or the computer to see the

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Speaker 1: information that causes bias. So we had to not let

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

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Speaker 1: things 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 is,

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Speaker 1: when you remove these factors, it happens naturally. And we

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

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Speaker 1: trillion matching events go through our system and almost it's

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

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Speaker 1: a lot people are very diverse. If you will just

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

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

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

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

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

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

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

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

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

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

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Speaker 1: now we have to neutralize these gender specific words inside

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Speaker 1: resume so that a person cannot look at the document

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Speaker 1: and still sus out ethnicity, gender and other biasing attributes.

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Speaker 1: It's remarkable that after hiding prejudicial information from the computer,

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Speaker 1: like a candidate's gender or ethnicity. A qualified, diverse workforce

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Speaker 1: assembled naturally as a result for the overburdened recruiter. That

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Speaker 1: means there are huge advantages to using intelligent automation. That's

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Speaker 1: a win win. As the conversation continues, Angela explains how

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Speaker 1: 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 automated,

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Speaker 1: and they're now they're like, I don't want to use it.

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Speaker 1: The level of innovation that has happened is absolutely incredible,

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Speaker 1: and for them to not try something because they tried

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Speaker 1: something a decade ago and it didn't work, that's just

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Speaker 1: completely the wrong approach. We're going to see massive innovation

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Speaker 1: over the next five to ten years too, and you

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Speaker 1: don't want to miss that. You don't want to say,

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Speaker 1: oh I said on the sidelines because I had a

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Speaker 1: bad experience a decade ago. So I think, if you know,

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

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Speaker 1: need to be part of this. This is your economy

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Speaker 1: in play a role. So what is a digital employee? Right?

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Speaker 1: So our partnership with IBM, Watson Orchestrate is around the

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Speaker 1: dig So D I, G. E. Y is a diggy

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Speaker 1: who's a digital employee. And I always think of it honestly,

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Speaker 1: is more of a concierge. You can have all of

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Speaker 1: your job descriptions living inside a box, for instance, and

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Speaker 1: so there's all the job descriptions and you're like, oh,

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Speaker 1: I need to find someone for this job. Watson goes

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Speaker 1: into box, grabs the job description, and then sends that

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Speaker 1: into this way system and this way automatically surfaces up

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Speaker 1: to three hundred qualified people from diverse organizations. Right, So

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Speaker 1: now the recruiter has not had to figure out where

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Speaker 1: are they going to source these people from. They haven't

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Speaker 1: had to sort out how they're going to reach out

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Speaker 1: to diverse organizations because we have partners and so now

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Speaker 1: that part has been taken care of, and then Watson

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Speaker 1: Organistrate does the next step, which is sends out communication

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Speaker 1: to the candidates that you are interested in automatically, and

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Speaker 1: then you get to sit and wait for these people

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Speaker 1: to respond back to you of their interest in discussing

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Speaker 1: something with you. Now all of this has been automated,

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Speaker 1: and essentially what I just described could easily take a

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Speaker 1: person in three weeks to go and identify all the talent.

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Speaker 1: So you take three weeks and you put this down

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Speaker 1: to roughly three or four minutes. Now it's absolutely incredible,

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Speaker 1: and I think it gives recruiters the time to do

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Speaker 1: what they really want to do, which is talked to people.

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Speaker 1: How did you decide that automation was the right tool

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Speaker 1: to fight bias? That was a journey, as I think

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Speaker 1: a lot of entrepreneurship is an innovation. When we hire technology,

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Speaker 1: we're hiring technology to do a job for us. So

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Speaker 1: what is the job to be done here? It is

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Speaker 1: to identify qualified talent without bias. So when you start

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Speaker 1: breaking this down, you realize that if humans could do it,

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

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Speaker 1: to have this happen for many, many years, and we

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Speaker 1: were not successful at it. And the reason why is

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

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

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

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Speaker 1: need these survival skills. That's part of bias. So we're

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Speaker 1: not going to get rid of it. And it's not

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

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Speaker 1: are human. And the best purpose that I think technology

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

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Speaker 1: things that we can't do. We have to be very

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Speaker 1: careful about how we engineer it. Our own technology was

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Speaker 1: engineered with removing bias is the priority. But we can

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Speaker 1: really have technology make us better humans because it can

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

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Speaker 1: improve the way we hire, most companies still think automation

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

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

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

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Speaker 1: automation can deliver for businesses today and how a company's

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

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Speaker 1: have to think of as employers. We have to think

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Speaker 1: everyone person looking for a job. Okay, so that is

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

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

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Speaker 1: be looking for passive talent. You need to be cultivating

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Speaker 1: able to say, we use the best technology to identify

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Speaker 1: you because you were special, and we really want you

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

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Speaker 1: you need to say. And when you get here, we're

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Speaker 1: going to help you automate those parts of your job

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Speaker 1: to be able to dig in in the areas you're

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

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

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

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Speaker 1: I think has been maybe the most prizing is that

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Speaker 1: it's really opened up the communication between hiring managers and

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Speaker 1: recruiters inside the same company. And there has long been

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Speaker 1: a silo of hiring managers putting out job descriptions and

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Speaker 1: saying recruiters, you know, go find people that make this,

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Speaker 1: and then the recruiter needs additional support because they're getting

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Speaker 1: getting those answers from the hiring manager. Hire managers very

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Speaker 1: by making this more efficient, you start getting much better

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Speaker 1: interactions between the entire company. And in this current market,

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

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Speaker 1: The people want to be found, and now the technology

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Speaker 1: is there to help make this seamless. So that's the

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Speaker 1: automation piece. Let's talk about the day city piece sort

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Speaker 1: of you know, landing here right. So on the diversity side,

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Speaker 1: how does how does a diverse workforce help make a

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Speaker 1: business more creative. A lot of the big consulting firms

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Speaker 1: have dug in for the last decade and said, is

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Speaker 1: there really an R o I around diversity, And uniformly

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Speaker 1: the answer has been yes. There is increased profits, a

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Speaker 1: more consistent workforce, meaning people don't want to leave. There's

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Speaker 1: not the same level of attrition when the workforce is

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Speaker 1: more diverse, and better recruiting numbers. So all of that

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Speaker 1: is like the outcome. But I think the key thing

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Speaker 1: to understand is the why behind this. The why is

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Speaker 1: that when you're diverse, you come to solutions, and you

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Speaker 1: come to questions and challenges from a different perspective. And

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Speaker 1: when you have a diverse workforce that is collaborating and

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Speaker 1: bringing their creativity to the market and you are using

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Speaker 1: their insight to develop better solutions, You're going to create

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Speaker 1: better solutions. You're gonna going to get those solutions to

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Speaker 1: market faster. You're going to understand positioning of your value

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Speaker 1: proposition inside the market. All of these things happened with

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Speaker 1: far more clarity when you have a diverse workforce. You

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Speaker 1: mentioned earlier that you failed was it thirteen times? And

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Speaker 1: I'm curious if sort of getting through those failures and

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Speaker 1: working your way to success was a place where you

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Speaker 1: did some creative problem solving. I would say that would

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Speaker 1: be an understatement at moments. Uh, there are times where

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Speaker 1: you know, I just say, like thirteen failures kind of

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Speaker 1: in passing. But there were times where I felt like

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Speaker 1: I was close to breaking as an innovator. And the

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Speaker 1: fact that was like, there's just non solution for this,

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Speaker 1: that our team failures is incredibly gut wrening. But I

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Speaker 1: was fortunate I had very supportive investors and so we

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Speaker 1: got through it. Uh, And I'm very proud of the

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Speaker 1: company we are today because of those failures. So just

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Speaker 1: to to wrap up, let's let's talk a little bit

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

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Speaker 1: Let's talk a little bit about the future. I mean,

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Speaker 1: how do you think the hiring process will look in

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

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

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

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

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

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

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

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

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

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Speaker 1: the economy, we obviously are seeing people love to work remote.

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Speaker 1: I know when we have an active job that goes

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Speaker 1: out into our marketplace, and if it is remote and

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Speaker 1: also prioritize diversity, you will have twenty to thirty times

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Speaker 1: more applicants. So I think that we're going to start

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

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Speaker 1: keep as many jobs remote as possible, just because it

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Speaker 1: attracts talent that companies are really struggling to find right now.

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Speaker 1: And I think the level of automation is going to

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Speaker 1: continue to increase, that will continue to increase an investment

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Speaker 1: over the next five to ten years. In twenty years,

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Speaker 1: I think we will all look back and say, why

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Speaker 1: did we all do these crazy parts of our job?

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Speaker 1: Why didn't we automate those It's because we were waiting

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Speaker 1: for technology like Orchestrate provides. Do you have any specific

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Speaker 1: advice for businesses that want to incorporate technology and automation

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Speaker 1: in their in their business and their work. I would say,

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Speaker 1: realize that you use automation every day. You use AI

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Speaker 1: every day, So when you're using Google Maps or something

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Speaker 1: like that, you're you're using your smartphone, you're accessing this

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Speaker 1: kind of technology as a consumer, as an individual, There's

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Speaker 1: no reason why you should worry about adopting it as

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Speaker 1: a business, and don't feel intimidated by it. You are

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Speaker 1: absolutely ready to use it and your business is ready

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

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Speaker 1: 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 the 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: It's 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

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Speaker 1: Renaud smart Talks with IBM is produced by Matt Romano,

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Speaker 1: David jaw, Royston Deserve and Edith Russelo with Jacob Goldstein.

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Speaker 1: Were edited by Sophie crane Are. Engineers are Jason Gambrel,

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Speaker 1: Sarah Brugare and Ben Holliday. Theme song by Granmasco. Special

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Speaker 1: thanks to Carli Migliori, Andy Kelly, Kathy Callaghan and the

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Speaker 1: Eight Bar and IBM teams, as well as the Pushkin

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Speaker 1: marketing team. Smart Talks with IBM is a production of

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Speaker 1: Pushkin Industries and i Heeart Media. To find more Pushkin podcasts,

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Speaker 1: listen on the I Heart Radio app, Apple Podcasts, or

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Speaker 1: wherever you listen to podcasts. I'm Malcolm Gladwell. This is

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Speaker 1: a paid advertisement from IBM.