Speaker 1: Welcome to tex Stuff, a production from I Heart Radio.
Speaker 1: This season of Smart Talks with IBM is all about
Speaker 1: new creators, the developers, data scientists, c t o s
Speaker 1: and other visionaries creatively applying technology in business to drive change.
Speaker 1: They use their knowledge and creativity to develop better ways
Speaker 1: of working, no matter the industry. Join hosts from your
Speaker 1: favorite Pushkin Industries podcasts as they use their expertise to
Speaker 1: deepen these conversations, and of course Malcolm Gladwell will guide
Speaker 1: you through the season as your host and provide his
Speaker 1: thoughts and analysis along the way. Look out for new
Speaker 1: episodes of Smart Talks with IBM on the I Heart
Speaker 1: Radio app, Apple Podcasts, or wherever you get your podcasts,
Speaker 1: and learn more at IBM dot com slash smart talks. Hello, Hello,
Speaker 1: Welcome to Smart Talks with IBM, a podcast from Pushkin Industries,
Speaker 1: I Heart Radio and ib AM. I'm Malcolm Gladmow. This season,
Speaker 1: we're talking to new creators, the developers, data scientists, c
Speaker 1: t o s and other visionaries who are creatively applying
Speaker 1: technology in business to drive change. Channeling their knowledge and expertise,
Speaker 1: they're developing more creative and effective solutions no matter the industry.
Speaker 1: Our guest today is Angela Hood, founder and CEO of
Speaker 1: This Way Global. Angela's mission is to eliminate discrimination in
Speaker 1: the hiring process. Angela is a serial entrepreneur who saw
Speaker 1: the potential to use automation technology as a way to
Speaker 1: combat the human biases that lead to unfair hiring practices
Speaker 1: and a less diverse, less competitive workforce. On today's show,
Speaker 1: you'll hear how automation makes it easier than ever to
Speaker 1: connect businesses with the right candidates, why automation is such
Speaker 1: a powerful tool to mitigate bias, and how Angela's own
Speaker 1: experiences with discriminatory hiring inspired her to take action. Angela
Speaker 1: spoke with Jacob Goldstein, host of the pushkin podcast What's
Speaker 1: Your Problem and former host of nprs Planet Money. Jacob
Speaker 1: has been a business journalist for over a decade, reporting
Speaker 1: for NPR, The Wall Street Journal, the Miami Herald, and
Speaker 1: is the author of the book Money, The True Story
Speaker 1: of a Made Up Thing. Okay, let's get to the interview.
Speaker 1: Can you tell me just you know we're gonna get
Speaker 1: into it a lot, but very briefly, what is this
Speaker 1: weay Global? So our technology is built specifically to match
Speaker 1: all people to all jobs without bias. And the last
Speaker 1: part is the hardest part and also the most important.
Speaker 1: And where did the idea for the company come from?
Speaker 1: So I'm a female engineer, and um, you know, I'm
Speaker 1: going out into the workforce after graduating, and I think
Speaker 1: that just like everyone else, I can just put my
Speaker 1: name up at the top of my resume and submit
Speaker 1: this to companies and they'll know entertain me for an interview.
Speaker 1: And what I found was because of the type of
Speaker 1: engineering role I was looking for, which is in the
Speaker 1: construction industry, that was not the case. The recruiters and
Speaker 1: also the hiring managers at these companies would see my
Speaker 1: name and think, well, I don't think that we want
Speaker 1: a woman, or we don't think that she really understands
Speaker 1: the job because it's out in the field. And so
Speaker 1: a mentor of mine said, why don't you use your initials,
Speaker 1: which are conveniently a L. And so I would submit
Speaker 1: my resume as a L hood and people thought I
Speaker 1: was a man. And so then at the same company,
Speaker 1: for the same job, I would get interviews. And that
Speaker 1: was the first moment where I realized there was a
Speaker 1: lot of bias in the market and turns out that
Speaker 1: there's a lot more biases, and we work to correct
Speaker 1: for all of them. It's funny that kind of story
Speaker 1: shouldn't be shocking, right, Like I know that I shouldn't
Speaker 1: be shocked by it, and yet I still kind of am.
Speaker 1: So clearly there's a tremendous amount of bias in the world,
Speaker 1: and bias in recruiting. And you know, we're familiar with
Speaker 1: these kind of stories of human bias, but but now
Speaker 1: there's this new problem, right, which is algorithmic bias. What
Speaker 1: is that tell me about algorithmic bias? A lot of
Speaker 1: algorithms are underpinned by machine learning, and machine learning very
Speaker 1: simply can happen kind of two different ways. You study
Speaker 1: what has happened in the past, and you try to
Speaker 1: duplicate that faster and more efficiently, and so that in
Speaker 1: this context would be called supervised learning, and that seemed
Speaker 1: like the logical place for nearly every recruiting technology to
Speaker 1: start the problem with that is that there's been so
Speaker 1: much historical bias that all you would really be doing
Speaker 1: is capturing that company or that hiring manager recruiters bias
Speaker 1: and duplicating it really fast, very efficiently. So you would
Speaker 1: just be expanding bias much much faster than how a
Speaker 1: human could. The flip side is something that's called unsupervised.
Speaker 1: So this is where you build a system essentially a
Speaker 1: black box. It's doing all types of calculations and decisions internally,
Speaker 1: and then it's not biased in theory, but you have
Speaker 1: no idea what it's basing its opinions on, so it
Speaker 1: can kind of create bizarre results. Also, it's not explainable,
Speaker 1: and so then you get caught in this catch twenty
Speaker 1: two of I don't want to do bias at scale,
Speaker 1: but I need to be explainable. So what do you do?
Speaker 1: After thirteen failures, we finally figured out a way to
Speaker 1: do this. The methodology that we finally found that innerrated
Speaker 1: the results that run biased was not ever allowing the
Speaker 1: math model or the computer to see the information that
Speaker 1: causes bias. So we had to not let gender enter
Speaker 1: into the system, ethnicity couldnot enter into the system, things
Speaker 1: like that, and so then the logical question is, okay, well,
Speaker 1: so if you don't allow those pieces of information to
Speaker 1: come in, how can you then enable qualified people that
Speaker 1: are also diverse to surface without bias? And the answer
Speaker 1: is when you remove these factors. It happens naturally, and
Speaker 1: we learned this by testing. We've had fifteen and a
Speaker 1: half trillion matching events go through our system and almost
Speaker 1: it's been now almost a decade, and through this we've
Speaker 1: learned a lot. People are very diverse. If you will
Speaker 1: just remove your own bias, you'll start seeing them. So
Speaker 1: it's a it's an automated version of what you as
Speaker 1: an individual did, uh before you started the company, when
Speaker 1: you switch from putting your full name on your applications
Speaker 1: to just your initials, effectively hiding your gender. Yeah, it
Speaker 1: started with that. What we also learned though, is even
Speaker 1: if you conceal your name, there are words where maybe
Speaker 1: someone is a waitress in a previous job, and so
Speaker 1: then the persons like, ah, that's a female, right, So
Speaker 1: then we had to go one step further. We had
Speaker 1: to say, now we have to neutralize these gender specific
Speaker 1: words inside resume so that a person cannot look at
Speaker 1: the document and still sus out ethnicity, gender and other
Speaker 1: biasing attributes. It's remarkable that after hiding prejudicial information from
Speaker 1: the computer like a candidate's gender or ethnicity, a qualified
Speaker 1: diverse workforce assembled naturally as a result, for the overburdened recruiter.
Speaker 1: That means there are huge advantage is to using intelligent automation.
Speaker 1: That's a win win. As the conversation continues, Angela explains
Speaker 1: how IBMS technology enabled her to simplify her customers hiring processes,
Speaker 1: and she also shed some light on how far intelligent
Speaker 1: automation has come in the past few years. How does
Speaker 1: intelligent automation look different today than it did, say, five
Speaker 1: years ago, it actually works, as the first thing. The
Speaker 1: the level of innovation that has taken place is absolutely incredible.
Speaker 1: And here's the thing about it is people have had
Speaker 1: some negative interactions with things that said that they were
Speaker 1: automated and they're now they're like, I don't want to
Speaker 1: use it. The level of innovation that has happened is
Speaker 1: absolutely incredible. And for them to not try something because
Speaker 1: they tried something a decade ago and it didn't work,
Speaker 1: that it's just completely the wrong approach. We're going to
Speaker 1: see massive innovation over the next five to ten years too,
Speaker 1: and you don't want to miss that. You don't want
Speaker 1: to say, oh, I said on the sidelines because I
Speaker 1: had a bad experience a decade ago. So I think
Speaker 1: if you know, if you're anywhere involved in technology or
Speaker 1: business growth, you need to be part of this. This
Speaker 1: is your economy in play a role. So what is
Speaker 1: a digital employee? Right? So our partnership with IBM, Watson
Speaker 1: Orchestrate is around the DIG. So D I, G. E.
Speaker 1: Y is a DIG who's a digital employee And I
Speaker 1: always think of it honestly, is more of a concierge.
Speaker 1: You can have all of your job descriptions living inside
Speaker 1: of Box, for instance, and so there's all the job
Speaker 1: descriptions and you're like, oh, I need to find someone
Speaker 1: for this job. Watson goes into Box, grabs the odd
Speaker 1: description and then sends that into this way system and
Speaker 1: this way automatically surfaces up to three hundred qualified people
Speaker 1: from diverse organizations. Right, so now the recruiter has not
Speaker 1: had to figure out where are they going to source
Speaker 1: these people from. They haven't had to sort out how
Speaker 1: they're going to reach out to diverse organizations because we
Speaker 1: have partners. And so now that part has been taken
Speaker 1: care of, and then Wat's an organestrate does the next step,
Speaker 1: which is sends out communication to the candidates that you
Speaker 1: are interested in automatically, and then you get to sit
Speaker 1: and wait for these people to respond back to you
Speaker 1: of their interest in discussing something with you. Now all
Speaker 1: of this has been automated, and essentially what I just
Speaker 1: described could easily take a person three weeks to go
Speaker 1: and identify all the talent. So you take three weeks
Speaker 1: and you put this down to roughly three or four minutes.
Speaker 1: Now it's absolutely incredible, and I think it gives recruiters
Speaker 1: the time to do what they really want to do,
Speaker 1: which is talked to people. How did you decide that
Speaker 1: automation was the right tool to fight bias? That was
Speaker 1: a journey, be as I think a lot of entrepreneurship
Speaker 1: is an innovation. When we hire technology, we're hiring technology
Speaker 1: to do a job for us. So what is the
Speaker 1: job to be done here? It is to identify qualified
Speaker 1: talent without bias. So when you start breaking this down,
Speaker 1: you realize that if humans could do it, we would
Speaker 1: have already done it. There's been a desire to have
Speaker 1: this happen for many, many years, and we were not
Speaker 1: successful at it. And the reason why is bias is
Speaker 1: not discrimination. These things get confused all the time. Bias
Speaker 1: is a product of our survival mechanism. We are always
Speaker 1: going to survive as humans, and so we we need
Speaker 1: these survival skills. That's part of bias. So we're not
Speaker 1: going to get rid of it, and it's not a
Speaker 1: character flaw. Bias is just inherently human and we are human.
Speaker 1: And the best purpose that I think technology can serve
Speaker 1: is the fact that it can do some things that
Speaker 1: we can't do. We have to be very careful about
Speaker 1: how we engineer it. Our own technology was engineered with
Speaker 1: removing bias is the priority. But we can really have
Speaker 1: technology make us better humans because it can do things
Speaker 1: we can't do. Despite the potential to vastly improve the
Speaker 1: way we hire, most companies still think automation is inaccessible,
Speaker 1: perhaps a luxury to aspire to in the future, but
Speaker 1: we live in a time when companies are hungrier than
Speaker 1: ever to fill positions quickly. Jacob asked Angela what automation
Speaker 1: can deliver for businesses today and how a companies creative
Speaker 1: It is linked with its diversity. How prevalent is intelligent
Speaker 1: automation in talent acquisition workflows today? So our data says
Speaker 1: that in enterprise that roughly seven percent have adopted some
Speaker 1: level of truly automated technology. But when you look at
Speaker 1: the job market in toll like, you know, if you
Speaker 1: look at the millions of employers we have, it's you know,
Speaker 1: less than three percent have adopted automation. These are companies
Speaker 1: that have a smaller workforce to do a great amount
Speaker 1: of work. They're recovering from a pandemic, they need help,
Speaker 1: and they think that automation is expensive, and it's actually
Speaker 1: the opposite. It's not expensive at all. And so I
Speaker 1: would encourage businesses that are mid market in small businesses
Speaker 1: to embrace technology in a way that they haven't done. So,
Speaker 1: I mean, there's one more piece of sort of what's
Speaker 1: going on now that seems really interesting in the context
Speaker 1: of what you do, and that is the incredible demand
Speaker 1: for workers right now. Right there's I don't know, ten
Speaker 1: million plus job openings, there's the great resignation, and so
Speaker 1: I'm curious how automation is helping both companies and workers
Speaker 1: through this process. Now, there's never been a job market
Speaker 1: like we are living in right now, and so we
Speaker 1: have to think of as employers, we have to think
Speaker 1: of how do I attract this talent. The other thing
Speaker 1: about the volume of jobs that are open. Is if
Speaker 1: you just do the simple math, there's two jobs for
Speaker 1: every one person looking for a job. Okay, so that
Speaker 1: is astounding to begin with. But of the jobs that
Speaker 1: we have available in the market, most people do not
Speaker 1: have the skill set required to fill those jobs. Inside
Speaker 1: the talent pool that is actively looking for a job.
Speaker 1: So now you have to go out and you need
Speaker 1: to be looking for passive talent. You need to be
Speaker 1: cultivating a relationship with the people that do have the
Speaker 1: skills you need. When you go to them, you need
Speaker 1: to be able to say two things. You need to
Speaker 1: be able to say, we use the best technology to
Speaker 1: identify you because you were special, and we really want
Speaker 1: you to come to work for us. That's number one.
Speaker 1: Two you need to say. And when you get here,
Speaker 1: we're going to help you automate those parts of your
Speaker 1: job that you've never really enjoyed before, because we want
Speaker 1: you to be able to dig in in the areas
Speaker 1: you're passionate about, because you're gonna be happier and you're
Speaker 1: gonna have a better work life balance. That is how
Speaker 1: you win talent in this market. Yeah, what have you
Speaker 1: heard back from recruiters about about this? You know, increased
Speaker 1: integration of technology. So one of the things that I
Speaker 1: think has been maybe the most surprising is that it's
Speaker 1: really opened up the communication between hiring managers and recruiters
Speaker 1: inside the same company. And there has long been a
Speaker 1: silo of hiring managers putting out job descriptions and saying recruiters,
Speaker 1: you know, go find people that make this. And then
Speaker 1: the recruiter needs additional support because they're getting questions from
Speaker 1: the candidates or there's some questions around what are the
Speaker 1: real job specific requirements, and they have trouble getting those
Speaker 1: answers from the hiring manager. Hiring managers very busy and
Speaker 1: they have their own job to do. So by making
Speaker 1: this more efficient, you start getting much better interactions between
Speaker 1: the entire company. And in this current market, companies are
Speaker 1: truly desperate to find the talent that they need. The
Speaker 1: people want to be found, and now the technology is
Speaker 1: there to help make this seamless. So that's the automation piece.
Speaker 1: Let's talk about the diversity piece sort of you know,
Speaker 1: landing here right. So on the diversity side, how does
Speaker 1: how does a diverse workforce help make a business more creative?
Speaker 1: A lot of the big consulting firms have dug in
Speaker 1: for the last decade and said is there really an
Speaker 1: R O I around diversity, And uniformly the answer has
Speaker 1: been yes. There is increased profits, a more consistent workforce,
Speaker 1: meaning people don't want to leave. There's not the same
Speaker 1: level of attrition when the workforce is more diverse, and
Speaker 1: better recruiting numbers. So all of that is like the outcome.
Speaker 1: But I think the key thing to understand is the
Speaker 1: why behind this. The why is that when you're diverse,
Speaker 1: you come to solutions and you come to questions and
Speaker 1: challenges from a different perspective. And when you have a
Speaker 1: diverse workforce that is collaborating and bringing their creativity to
Speaker 1: the market and you are using their insight to develop
Speaker 1: better solutions, You're going to create better solutions. You're going
Speaker 1: to get those solutions to market faster. You're going to
Speaker 1: understand positioning of your value proposition inside the market. All
Speaker 1: of these things happened with far more clarity when you
Speaker 1: have a diverse workforce. You mentioned earlier that you failed
Speaker 1: was it thirteen times? And I'm curious if sort of
Speaker 1: getting through those failures and working your way to success
Speaker 1: was a place where you did some creative problem solving.
Speaker 1: I would say that would be an understatement at moments. Uh,
Speaker 1: there are times where you know, I just say, like
Speaker 1: thirteen failures kind of in passing. But there were times
Speaker 1: where I felt like I was close to breaking as
Speaker 1: an innovator, and the fact that was, like, there's just
Speaker 1: non solution for this. The thirteen failures is incredibly gut wrenching.
Speaker 1: But I was fortunate. I had very supportive and susters
Speaker 1: and so we got through it. Uh, And I'm very
Speaker 1: proud of the company we are today because of those failures.
Speaker 1: So just to to wrap up, let's let's talk a
Speaker 1: little bit about the future. We've done the past, we've
Speaker 1: done the present. Let's talk a little bit about the future.
Speaker 1: I mean, how do you think the hiring process will
Speaker 1: look in the future, whatever, five years, ten years, And
Speaker 1: in particular, what role will will automation, intelligent automation, augmented intelligence,
Speaker 1: what role will will all that play? Well, if you
Speaker 1: look back in decades ago, there were people that would
Speaker 1: work for the same company for ten twenty years, and
Speaker 1: that was, you know, not that unusual. Now very uncommon,
Speaker 1: and in the future, I think it will be absolutely rare.
Speaker 1: I think we're looking more likely at people that will
Speaker 1: work for multiple companies. We're seeing that with the rise
Speaker 1: of the gig economy, we obviously are seeing people love
Speaker 1: to work remote. I know when we have an active
Speaker 1: job that goes out into our marketplace and if it
Speaker 1: is remote and also prioritize diversity, you will have twenty
Speaker 1: to thirty times more applicants. So I think that we're
Speaker 1: going to start seeing companies really investing in those two attributes,
Speaker 1: trying to keep as many jobs remote as possible, just
Speaker 1: because it attracts talent that companies are really struggling to
Speaker 1: find right now. And I think the level of automation
Speaker 1: is going to continue to increase, that will continue to
Speaker 1: increase an investment over the next five to ten years.
Speaker 1: In twenty years, I think we will all look back
Speaker 1: and say, why did we all do these crazy parts
Speaker 1: of our job? Why didn't we automate those It's because
Speaker 1: we were waiting for technology like Orchestrate provides. Do you
Speaker 1: have any specific advice for businesses that want to incorporate
Speaker 1: technology and automation in their in their business and their work.
Speaker 1: I would say, realize that you use automation every day,
Speaker 1: You use AI every day. So when you're using Google
Speaker 1: Maps or something like that, you're you're using your smartphone.
Speaker 1: You're accessing this kind of technology as a consumer, as
Speaker 1: an individual, there's no reason why you should worry about
Speaker 1: adopting it as a business, and don't feel intimidated by it.
Speaker 1: You are absolutely ready to use it and your business
Speaker 1: is ready to benefit from it. Just don't have that fear.
Speaker 1: We certainly is a company work with companies of all sizes.
Speaker 1: We have companies that have five to tend employees only,
Speaker 1: and we have some that have hundreds of thousands employees.
Speaker 1: That's a great thing about automation is it doesn't care
Speaker 1: the size of your company. It will work for you.
Speaker 1: Angela's fun to talk to you. Thank you for your time,
Speaker 1: congratulations and making it through to thirty And if you
Speaker 1: really think about that, that's a It's a really impressive
Speaker 1: level of persistence. Like I could imagine failing a few times,
Speaker 1: but I would have given up at nine or something
Speaker 1: at seven. At seven, I was like, I'm a crazy person.
Speaker 1: It is vitally important to get hiring right. What could
Speaker 1: be more essential to an organization's success than deciding which
Speaker 1: human beings make up that organization. If we let our
Speaker 1: biases go unchecked, we end up excluding qualified candidates, leaving
Speaker 1: our workforce is less diverse and therefore less competitive because
Speaker 1: of it. Angela made an interesting point earlier that I
Speaker 1: want to go back to. She said that bias is
Speaker 1: not a character flaw, it's a survival instinct, and that
Speaker 1: the best purpose technology can serve is to make us
Speaker 1: better humans by doing things for us that we can't.
Speaker 1: Bias is in human nature and we'll never truly get
Speaker 1: rid of it, but the first step to minimizing its
Speaker 1: impact is to acknowledge it's a problem we need help with.
Speaker 1: Intelligent automation can make hiring more efficient. When we allow
Speaker 1: computers to mitigate our biases, better hiring is the result.
Speaker 1: Sometimes to build the best team possible, we have to
Speaker 1: know when to listen to our human instincts and when
Speaker 1: to set them aside. On the next episode of Smart
Speaker 1: Talks with IBM, how to use data creatively in order
Speaker 1: to solve novel problems, We talk with YouTube content creator
Speaker 1: and IBM's senior Data science and AI technical specialist, Nicholas Renaud.
Speaker 1: Smart Talks with IBM is produced by Matt Romano, David
Speaker 1: jaw Royston Deserve and Edith Rousselo with Jacob Goldstein were
Speaker 1: edited by Sophie Crane. Are Engineers are Jason Gambrel, Sarah
Speaker 1: Bragair and Ben Tolliday. Theme song by Granmascope. Special thanks
Speaker 1: to Carlie mcglory, Andy Kelly, Kathy Callaghan and the eight
Speaker 1: Bar and IBM teams, as well as the Pushkin marketing team.
Speaker 1: Smart Talks with IBM is a production of Pushkin Industries
Speaker 1: and I Heart Media. To find more Pushkin podcasts, listen
Speaker 1: on the i Heart Radio app, Apple Podcasts, or wherever
Speaker 1: you listen to podcasts. I'm Malcolm Gladwell. This is a
Speaker 1: paid advertisement from IBM.