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