WEBVTT - Combatting Hiring Bias: Recruiting a Diverse Workforce with Intelligent Automation 0:00:04.400 --> 0:00:07.800 Welcome to tex Stuff, a production from I Heart Radio. 0:00:11.840 --> 0:00:14.480 This season of Smart Talks with IBM is all about 0:00:14.600 --> 0:00:18.480 new creators, the developers, data scientists, c t o s 0:00:18.560 --> 0:00:23.280 and other visionaries creatively applying technology in business to drive change. 0:00:23.800 --> 0:00:26.680 They use their knowledge and creativity to develop better ways 0:00:26.680 --> 0:00:30.280 of working, no matter the industry. Join hosts from your 0:00:30.320 --> 0:00:33.880 favorite Pushkin Industries podcasts as they use their expertise to 0:00:33.960 --> 0:00:37.480 deepen these conversations, and of course Malcolm Gladwell will guide 0:00:37.520 --> 0:00:39.839 you through the season as your host and provide his 0:00:39.920 --> 0:00:43.000 thoughts and analysis along the way. Look out for new 0:00:43.040 --> 0:00:45.480 episodes of Smart Talks with IBM on the I Heart 0:00:45.560 --> 0:00:49.159 Radio app, Apple Podcasts, or wherever you get your podcasts, 0:00:49.360 --> 0:00:56.360 and learn more at IBM dot com slash smart talks. Hello, Hello, 0:00:56.520 --> 0:01:00.560 Welcome to Smart Talks with IBM, a podcast from Pushkin Industries, 0:01:00.800 --> 0:01:05.679 I Heart Radio and ib AM. I'm Malcolm Gladmow. This season, 0:01:05.720 --> 0:01:10.000 we're talking to new creators, the developers, data scientists, c 0:01:10.200 --> 0:01:13.440 t o s and other visionaries who are creatively applying 0:01:13.520 --> 0:01:18.200 technology in business to drive change. Channeling their knowledge and expertise, 0:01:18.360 --> 0:01:23.240 they're developing more creative and effective solutions no matter the industry. 0:01:23.520 --> 0:01:27.200 Our guest today is Angela Hood, founder and CEO of 0:01:27.360 --> 0:01:31.840 This Way Global. Angela's mission is to eliminate discrimination in 0:01:31.880 --> 0:01:35.760 the hiring process. Angela is a serial entrepreneur who saw 0:01:35.800 --> 0:01:39.480 the potential to use automation technology as a way to 0:01:39.600 --> 0:01:43.720 combat the human biases that lead to unfair hiring practices 0:01:44.000 --> 0:01:48.559 and a less diverse, less competitive workforce. On today's show, 0:01:48.720 --> 0:01:51.880 you'll hear how automation makes it easier than ever to 0:01:51.920 --> 0:01:55.840 connect businesses with the right candidates, why automation is such 0:01:55.880 --> 0:01:59.480 a powerful tool to mitigate bias, and how Angela's own 0:01:59.520 --> 0:02:05.200 experiences with discriminatory hiring inspired her to take action. Angela 0:02:05.240 --> 0:02:09.080 spoke with Jacob Goldstein, host of the pushkin podcast What's 0:02:09.120 --> 0:02:13.480 Your Problem and former host of nprs Planet Money. Jacob 0:02:13.520 --> 0:02:16.799 has been a business journalist for over a decade, reporting 0:02:16.840 --> 0:02:20.440 for NPR, The Wall Street Journal, the Miami Herald, and 0:02:20.560 --> 0:02:23.720 is the author of the book Money, The True Story 0:02:23.880 --> 0:02:27.160 of a Made Up Thing. Okay, let's get to the interview. 0:02:30.760 --> 0:02:33.320 Can you tell me just you know we're gonna get 0:02:33.360 --> 0:02:36.280 into it a lot, but very briefly, what is this 0:02:36.320 --> 0:02:41.960 weay Global? So our technology is built specifically to match 0:02:42.040 --> 0:02:45.560 all people to all jobs without bias. And the last 0:02:45.639 --> 0:02:49.040 part is the hardest part and also the most important. 0:02:49.600 --> 0:02:52.799 And where did the idea for the company come from? 0:02:52.840 --> 0:02:56.600 So I'm a female engineer, and um, you know, I'm 0:02:56.600 --> 0:03:00.800 going out into the workforce after graduating, and I think 0:03:00.840 --> 0:03:02.920 that just like everyone else, I can just put my 0:03:03.040 --> 0:03:06.080 name up at the top of my resume and submit 0:03:06.200 --> 0:03:10.840 this to companies and they'll know entertain me for an interview. 0:03:11.560 --> 0:03:14.120 And what I found was because of the type of 0:03:14.280 --> 0:03:16.880 engineering role I was looking for, which is in the 0:03:16.919 --> 0:03:21.240 construction industry, that was not the case. The recruiters and 0:03:21.280 --> 0:03:24.079 also the hiring managers at these companies would see my 0:03:24.240 --> 0:03:26.560 name and think, well, I don't think that we want 0:03:26.600 --> 0:03:29.160 a woman, or we don't think that she really understands 0:03:29.200 --> 0:03:31.480 the job because it's out in the field. And so 0:03:31.639 --> 0:03:34.400 a mentor of mine said, why don't you use your initials, 0:03:34.560 --> 0:03:38.600 which are conveniently a L. And so I would submit 0:03:38.800 --> 0:03:43.040 my resume as a L hood and people thought I 0:03:43.080 --> 0:03:45.800 was a man. And so then at the same company, 0:03:45.840 --> 0:03:48.680 for the same job, I would get interviews. And that 0:03:48.760 --> 0:03:50.680 was the first moment where I realized there was a 0:03:50.680 --> 0:03:53.520 lot of bias in the market and turns out that 0:03:53.600 --> 0:03:57.200 there's a lot more biases, and we work to correct 0:03:57.240 --> 0:03:59.360 for all of them. It's funny that kind of story 0:03:59.360 --> 0:04:02.120 shouldn't be shocking, right, Like I know that I shouldn't 0:04:02.160 --> 0:04:05.440 be shocked by it, and yet I still kind of am. 0:04:05.480 --> 0:04:08.840 So clearly there's a tremendous amount of bias in the world, 0:04:09.000 --> 0:04:12.040 and bias in recruiting. And you know, we're familiar with 0:04:12.080 --> 0:04:15.920 these kind of stories of human bias, but but now 0:04:15.960 --> 0:04:21.479 there's this new problem, right, which is algorithmic bias. What 0:04:21.680 --> 0:04:25.360 is that tell me about algorithmic bias? A lot of 0:04:25.600 --> 0:04:30.960 algorithms are underpinned by machine learning, and machine learning very 0:04:31.000 --> 0:04:34.039 simply can happen kind of two different ways. You study 0:04:34.720 --> 0:04:37.479 what has happened in the past, and you try to 0:04:37.640 --> 0:04:42.840 duplicate that faster and more efficiently, and so that in 0:04:42.920 --> 0:04:48.520 this context would be called supervised learning, and that seemed 0:04:48.560 --> 0:04:52.719 like the logical place for nearly every recruiting technology to 0:04:52.800 --> 0:04:56.839 start the problem with that is that there's been so 0:04:56.920 --> 0:05:00.680 much historical bias that all you would really be doing 0:05:01.320 --> 0:05:06.400 is capturing that company or that hiring manager recruiters bias 0:05:07.120 --> 0:05:11.400 and duplicating it really fast, very efficiently. So you would 0:05:11.440 --> 0:05:15.400 just be expanding bias much much faster than how a 0:05:15.480 --> 0:05:21.240 human could. The flip side is something that's called unsupervised. 0:05:21.360 --> 0:05:24.680 So this is where you build a system essentially a 0:05:24.720 --> 0:05:29.240 black box. It's doing all types of calculations and decisions internally, 0:05:30.040 --> 0:05:33.640 and then it's not biased in theory, but you have 0:05:33.680 --> 0:05:37.159 no idea what it's basing its opinions on, so it 0:05:37.240 --> 0:05:42.760 can kind of create bizarre results. Also, it's not explainable, 0:05:43.400 --> 0:05:45.840 and so then you get caught in this catch twenty 0:05:45.839 --> 0:05:48.760 two of I don't want to do bias at scale, 0:05:49.160 --> 0:05:52.440 but I need to be explainable. So what do you do? 0:05:52.800 --> 0:05:56.200 After thirteen failures, we finally figured out a way to 0:05:56.240 --> 0:06:00.520 do this. The methodology that we finally found that innerrated 0:06:00.560 --> 0:06:05.480 the results that run biased was not ever allowing the 0:06:05.600 --> 0:06:10.320 math model or the computer to see the information that 0:06:10.480 --> 0:06:15.360 causes bias. So we had to not let gender enter 0:06:15.480 --> 0:06:20.160 into the system, ethnicity couldnot enter into the system, things 0:06:20.200 --> 0:06:23.960 like that, and so then the logical question is, okay, well, 0:06:24.000 --> 0:06:26.839 so if you don't allow those pieces of information to 0:06:26.920 --> 0:06:32.120 come in, how can you then enable qualified people that 0:06:32.160 --> 0:06:36.719 are also diverse to surface without bias? And the answer 0:06:36.839 --> 0:06:41.080 is when you remove these factors. It happens naturally, and 0:06:41.160 --> 0:06:44.440 we learned this by testing. We've had fifteen and a 0:06:44.480 --> 0:06:47.640 half trillion matching events go through our system and almost 0:06:47.960 --> 0:06:51.360 it's been now almost a decade, and through this we've 0:06:51.440 --> 0:06:54.760 learned a lot. People are very diverse. If you will 0:06:54.839 --> 0:06:58.480 just remove your own bias, you'll start seeing them. So 0:06:58.520 --> 0:07:01.520 it's a it's an automated version of what you as 0:07:01.560 --> 0:07:05.360 an individual did, uh before you started the company, when 0:07:05.400 --> 0:07:09.159 you switch from putting your full name on your applications 0:07:09.200 --> 0:07:13.320 to just your initials, effectively hiding your gender. Yeah, it 0:07:13.400 --> 0:07:16.320 started with that. What we also learned though, is even 0:07:16.360 --> 0:07:20.120 if you conceal your name, there are words where maybe 0:07:20.200 --> 0:07:23.280 someone is a waitress in a previous job, and so 0:07:23.400 --> 0:07:27.120 then the persons like, ah, that's a female, right, So 0:07:27.160 --> 0:07:29.040 then we had to go one step further. We had 0:07:29.040 --> 0:07:33.000 to say, now we have to neutralize these gender specific 0:07:33.080 --> 0:07:37.080 words inside resume so that a person cannot look at 0:07:37.080 --> 0:07:41.800 the document and still sus out ethnicity, gender and other 0:07:41.880 --> 0:07:47.720 biasing attributes. It's remarkable that after hiding prejudicial information from 0:07:47.760 --> 0:07:52.240 the computer like a candidate's gender or ethnicity, a qualified 0:07:52.360 --> 0:07:57.920 diverse workforce assembled naturally as a result, for the overburdened recruiter. 0:07:58.160 --> 0:08:02.200 That means there are huge advantage is to using intelligent automation. 0:08:02.720 --> 0:08:07.640 That's a win win. As the conversation continues, Angela explains 0:08:07.920 --> 0:08:13.400 how IBMS technology enabled her to simplify her customers hiring processes, 0:08:13.920 --> 0:08:17.360 and she also shed some light on how far intelligent 0:08:17.480 --> 0:08:21.960 automation has come in the past few years. How does 0:08:22.000 --> 0:08:25.800 intelligent automation look different today than it did, say, five 0:08:25.880 --> 0:08:33.160 years ago, it actually works, as the first thing. The 0:08:33.160 --> 0:08:38.800 the level of innovation that has taken place is absolutely incredible. 0:08:39.559 --> 0:08:42.640 And here's the thing about it is people have had 0:08:42.760 --> 0:08:45.600 some negative interactions with things that said that they were 0:08:45.640 --> 0:08:48.440 automated and they're now they're like, I don't want to 0:08:48.520 --> 0:08:52.760 use it. The level of innovation that has happened is 0:08:52.880 --> 0:08:56.960 absolutely incredible. And for them to not try something because 0:08:56.960 --> 0:08:59.280 they tried something a decade ago and it didn't work, 0:08:59.760 --> 0:09:02.959 that it's just completely the wrong approach. We're going to 0:09:03.080 --> 0:09:07.000 see massive innovation over the next five to ten years too, 0:09:07.480 --> 0:09:09.160 and you don't want to miss that. You don't want 0:09:09.160 --> 0:09:11.600 to say, oh, I said on the sidelines because I 0:09:11.600 --> 0:09:14.160 had a bad experience a decade ago. So I think 0:09:14.240 --> 0:09:17.520 if you know, if you're anywhere involved in technology or 0:09:18.240 --> 0:09:21.280 business growth, you need to be part of this. This 0:09:21.360 --> 0:09:26.200 is your economy in play a role. So what is 0:09:26.320 --> 0:09:32.720 a digital employee? Right? So our partnership with IBM, Watson 0:09:32.840 --> 0:09:37.280 Orchestrate is around the DIG. So D I, G. E. 0:09:37.640 --> 0:09:41.680 Y is a DIG who's a digital employee And I 0:09:41.840 --> 0:09:44.840 always think of it honestly, is more of a concierge. 0:09:45.000 --> 0:09:48.319 You can have all of your job descriptions living inside 0:09:48.360 --> 0:09:50.760 of Box, for instance, and so there's all the job 0:09:50.800 --> 0:09:54.240 descriptions and you're like, oh, I need to find someone 0:09:54.440 --> 0:10:00.240 for this job. Watson goes into Box, grabs the odd 0:10:00.240 --> 0:10:05.120 description and then sends that into this way system and 0:10:05.240 --> 0:10:10.280 this way automatically surfaces up to three hundred qualified people 0:10:10.800 --> 0:10:15.280 from diverse organizations. Right, so now the recruiter has not 0:10:15.440 --> 0:10:17.520 had to figure out where are they going to source 0:10:17.600 --> 0:10:21.199 these people from. They haven't had to sort out how 0:10:21.200 --> 0:10:24.440 they're going to reach out to diverse organizations because we 0:10:24.480 --> 0:10:28.320 have partners. And so now that part has been taken 0:10:28.360 --> 0:10:31.559 care of, and then Wat's an organestrate does the next step, 0:10:31.600 --> 0:10:36.199 which is sends out communication to the candidates that you 0:10:36.440 --> 0:10:41.480 are interested in automatically, and then you get to sit 0:10:41.559 --> 0:10:44.440 and wait for these people to respond back to you 0:10:44.600 --> 0:10:48.080 of their interest in discussing something with you. Now all 0:10:48.120 --> 0:10:51.040 of this has been automated, and essentially what I just 0:10:51.120 --> 0:10:55.080 described could easily take a person three weeks to go 0:10:55.120 --> 0:10:58.280 and identify all the talent. So you take three weeks 0:10:58.400 --> 0:11:01.800 and you put this down to roughly three or four minutes. 0:11:01.960 --> 0:11:06.439 Now it's absolutely incredible, and I think it gives recruiters 0:11:06.480 --> 0:11:08.320 the time to do what they really want to do, 0:11:08.360 --> 0:11:12.280 which is talked to people. How did you decide that 0:11:12.360 --> 0:11:18.160 automation was the right tool to fight bias? That was 0:11:18.200 --> 0:11:21.720 a journey, be as I think a lot of entrepreneurship 0:11:21.840 --> 0:11:26.480 is an innovation. When we hire technology, we're hiring technology 0:11:26.559 --> 0:11:28.800 to do a job for us. So what is the 0:11:28.880 --> 0:11:33.920 job to be done here? It is to identify qualified 0:11:34.000 --> 0:11:38.480 talent without bias. So when you start breaking this down, 0:11:39.080 --> 0:11:42.280 you realize that if humans could do it, we would 0:11:42.280 --> 0:11:45.280 have already done it. There's been a desire to have 0:11:45.440 --> 0:11:48.920 this happen for many, many years, and we were not 0:11:49.040 --> 0:11:52.400 successful at it. And the reason why is bias is 0:11:52.440 --> 0:11:55.880 not discrimination. These things get confused all the time. Bias 0:11:56.120 --> 0:12:00.480 is a product of our survival mechanism. We are always 0:12:00.480 --> 0:12:03.640 going to survive as humans, and so we we need 0:12:03.760 --> 0:12:07.080 these survival skills. That's part of bias. So we're not 0:12:07.080 --> 0:12:09.040 going to get rid of it, and it's not a 0:12:09.120 --> 0:12:14.400 character flaw. Bias is just inherently human and we are human. 0:12:15.120 --> 0:12:18.439 And the best purpose that I think technology can serve 0:12:18.679 --> 0:12:20.840 is the fact that it can do some things that 0:12:20.920 --> 0:12:23.920 we can't do. We have to be very careful about 0:12:24.000 --> 0:12:27.120 how we engineer it. Our own technology was engineered with 0:12:27.280 --> 0:12:31.760 removing bias is the priority. But we can really have 0:12:32.080 --> 0:12:34.920 technology make us better humans because it can do things 0:12:34.920 --> 0:12:38.680 we can't do. Despite the potential to vastly improve the 0:12:38.720 --> 0:12:43.520 way we hire, most companies still think automation is inaccessible, 0:12:44.120 --> 0:12:48.000 perhaps a luxury to aspire to in the future, but 0:12:48.120 --> 0:12:50.880 we live in a time when companies are hungrier than 0:12:50.880 --> 0:12:55.880 ever to fill positions quickly. Jacob asked Angela what automation 0:12:55.920 --> 0:12:59.959 can deliver for businesses today and how a companies creative 0:13:00.000 --> 0:13:04.640 It is linked with its diversity. How prevalent is intelligent 0:13:04.679 --> 0:13:10.559 automation in talent acquisition workflows today? So our data says 0:13:10.640 --> 0:13:17.199 that in enterprise that roughly seven percent have adopted some 0:13:17.320 --> 0:13:21.280 level of truly automated technology. But when you look at 0:13:21.480 --> 0:13:23.920 the job market in toll like, you know, if you 0:13:23.960 --> 0:13:27.080 look at the millions of employers we have, it's you know, 0:13:27.160 --> 0:13:32.959 less than three percent have adopted automation. These are companies 0:13:33.000 --> 0:13:37.320 that have a smaller workforce to do a great amount 0:13:37.320 --> 0:13:40.880 of work. They're recovering from a pandemic, they need help, 0:13:41.160 --> 0:13:45.800 and they think that automation is expensive, and it's actually 0:13:45.880 --> 0:13:49.000 the opposite. It's not expensive at all. And so I 0:13:49.240 --> 0:13:53.520 would encourage businesses that are mid market in small businesses 0:13:53.760 --> 0:13:57.160 to embrace technology in a way that they haven't done. So, 0:13:57.320 --> 0:13:59.920 I mean, there's one more piece of sort of what's 0:14:00.000 --> 0:14:02.800 going on now that seems really interesting in the context 0:14:02.800 --> 0:14:05.839 of what you do, and that is the incredible demand 0:14:06.080 --> 0:14:08.600 for workers right now. Right there's I don't know, ten 0:14:08.679 --> 0:14:12.440 million plus job openings, there's the great resignation, and so 0:14:12.480 --> 0:14:18.559 I'm curious how automation is helping both companies and workers 0:14:18.880 --> 0:14:24.720 through this process. Now, there's never been a job market 0:14:24.800 --> 0:14:27.560 like we are living in right now, and so we 0:14:27.720 --> 0:14:30.360 have to think of as employers, we have to think 0:14:30.400 --> 0:14:33.600 of how do I attract this talent. The other thing 0:14:33.600 --> 0:14:36.800 about the volume of jobs that are open. Is if 0:14:36.800 --> 0:14:39.800 you just do the simple math, there's two jobs for 0:14:39.840 --> 0:14:42.760 every one person looking for a job. Okay, so that 0:14:42.920 --> 0:14:47.040 is astounding to begin with. But of the jobs that 0:14:47.120 --> 0:14:50.840 we have available in the market, most people do not 0:14:51.160 --> 0:14:56.760 have the skill set required to fill those jobs. Inside 0:14:56.880 --> 0:14:59.600 the talent pool that is actively looking for a job. 0:15:00.600 --> 0:15:02.600 So now you have to go out and you need 0:15:02.640 --> 0:15:05.480 to be looking for passive talent. You need to be 0:15:05.600 --> 0:15:08.560 cultivating a relationship with the people that do have the 0:15:08.560 --> 0:15:11.640 skills you need. When you go to them, you need 0:15:11.680 --> 0:15:13.360 to be able to say two things. You need to 0:15:13.440 --> 0:15:16.880 be able to say, we use the best technology to 0:15:16.960 --> 0:15:20.760 identify you because you were special, and we really want 0:15:20.800 --> 0:15:22.920 you to come to work for us. That's number one. 0:15:23.600 --> 0:15:25.920 Two you need to say. And when you get here, 0:15:26.160 --> 0:15:28.760 we're going to help you automate those parts of your 0:15:28.840 --> 0:15:32.280 job that you've never really enjoyed before, because we want 0:15:32.320 --> 0:15:35.840 you to be able to dig in in the areas 0:15:35.880 --> 0:15:39.040 you're passionate about, because you're gonna be happier and you're 0:15:39.040 --> 0:15:41.920 gonna have a better work life balance. That is how 0:15:41.920 --> 0:15:44.400 you win talent in this market. Yeah, what have you 0:15:44.480 --> 0:15:48.440 heard back from recruiters about about this? You know, increased 0:15:48.480 --> 0:15:51.680 integration of technology. So one of the things that I 0:15:51.680 --> 0:15:55.200 think has been maybe the most surprising is that it's 0:15:55.280 --> 0:15:59.480 really opened up the communication between hiring managers and recruiters 0:15:59.560 --> 0:16:02.720 inside the same company. And there has long been a 0:16:02.800 --> 0:16:08.120 silo of hiring managers putting out job descriptions and saying recruiters, 0:16:08.160 --> 0:16:11.240 you know, go find people that make this. And then 0:16:11.480 --> 0:16:15.280 the recruiter needs additional support because they're getting questions from 0:16:15.280 --> 0:16:18.360 the candidates or there's some questions around what are the 0:16:18.480 --> 0:16:22.840 real job specific requirements, and they have trouble getting those 0:16:22.880 --> 0:16:27.720 answers from the hiring manager. Hiring managers very busy and 0:16:27.720 --> 0:16:31.080 they have their own job to do. So by making 0:16:31.120 --> 0:16:35.600 this more efficient, you start getting much better interactions between 0:16:35.600 --> 0:16:40.560 the entire company. And in this current market, companies are 0:16:41.040 --> 0:16:44.000 truly desperate to find the talent that they need. The 0:16:44.120 --> 0:16:47.360 people want to be found, and now the technology is 0:16:47.400 --> 0:16:51.440 there to help make this seamless. So that's the automation piece. 0:16:52.000 --> 0:16:54.640 Let's talk about the diversity piece sort of you know, 0:16:55.240 --> 0:16:59.200 landing here right. So on the diversity side, how does 0:16:59.680 --> 0:17:03.960 how does a diverse workforce help make a business more creative? 0:17:06.040 --> 0:17:09.400 A lot of the big consulting firms have dug in 0:17:09.520 --> 0:17:12.560 for the last decade and said is there really an 0:17:12.720 --> 0:17:17.600 R O I around diversity, And uniformly the answer has 0:17:17.640 --> 0:17:23.639 been yes. There is increased profits, a more consistent workforce, 0:17:23.760 --> 0:17:26.240 meaning people don't want to leave. There's not the same 0:17:26.320 --> 0:17:29.880 level of attrition when the workforce is more diverse, and 0:17:30.160 --> 0:17:33.840 better recruiting numbers. So all of that is like the outcome. 0:17:34.400 --> 0:17:37.119 But I think the key thing to understand is the 0:17:37.200 --> 0:17:41.400 why behind this. The why is that when you're diverse, 0:17:42.359 --> 0:17:45.960 you come to solutions and you come to questions and 0:17:46.040 --> 0:17:50.119 challenges from a different perspective. And when you have a 0:17:50.160 --> 0:17:55.919 diverse workforce that is collaborating and bringing their creativity to 0:17:56.560 --> 0:18:00.919 the market and you are using their insight to develop 0:18:01.000 --> 0:18:04.479 better solutions, You're going to create better solutions. You're going 0:18:04.800 --> 0:18:07.560 to get those solutions to market faster. You're going to 0:18:07.640 --> 0:18:12.200 understand positioning of your value proposition inside the market. All 0:18:12.240 --> 0:18:15.800 of these things happened with far more clarity when you 0:18:15.880 --> 0:18:20.240 have a diverse workforce. You mentioned earlier that you failed 0:18:20.359 --> 0:18:24.280 was it thirteen times? And I'm curious if sort of 0:18:24.600 --> 0:18:27.879 getting through those failures and working your way to success 0:18:28.600 --> 0:18:31.240 was a place where you did some creative problem solving. 0:18:33.520 --> 0:18:38.359 I would say that would be an understatement at moments. Uh, 0:18:38.520 --> 0:18:41.320 there are times where you know, I just say, like 0:18:41.359 --> 0:18:44.200 thirteen failures kind of in passing. But there were times 0:18:44.280 --> 0:18:48.480 where I felt like I was close to breaking as 0:18:48.560 --> 0:18:50.960 an innovator, and the fact that was, like, there's just 0:18:51.040 --> 0:18:57.040 non solution for this. The thirteen failures is incredibly gut wrenching. 0:18:57.280 --> 0:19:00.440 But I was fortunate. I had very supportive and susters 0:19:00.440 --> 0:19:02.760 and so we got through it. Uh, And I'm very 0:19:02.800 --> 0:19:05.600 proud of the company we are today because of those failures. 0:19:06.240 --> 0:19:09.119 So just to to wrap up, let's let's talk a 0:19:09.160 --> 0:19:12.240 little bit about the future. We've done the past, we've 0:19:12.320 --> 0:19:14.679 done the present. Let's talk a little bit about the future. 0:19:15.119 --> 0:19:18.480 I mean, how do you think the hiring process will 0:19:18.560 --> 0:19:21.760 look in the future, whatever, five years, ten years, And 0:19:21.800 --> 0:19:27.520 in particular, what role will will automation, intelligent automation, augmented intelligence, 0:19:27.560 --> 0:19:31.879 what role will will all that play? Well, if you 0:19:32.000 --> 0:19:36.160 look back in decades ago, there were people that would 0:19:36.200 --> 0:19:38.760 work for the same company for ten twenty years, and 0:19:38.800 --> 0:19:42.680 that was, you know, not that unusual. Now very uncommon, 0:19:42.800 --> 0:19:45.840 and in the future, I think it will be absolutely rare. 0:19:46.440 --> 0:19:49.680 I think we're looking more likely at people that will 0:19:49.720 --> 0:19:53.080 work for multiple companies. We're seeing that with the rise 0:19:53.119 --> 0:19:56.960 of the gig economy, we obviously are seeing people love 0:19:57.040 --> 0:20:00.960 to work remote. I know when we have an active 0:20:01.040 --> 0:20:05.000 job that goes out into our marketplace and if it 0:20:05.080 --> 0:20:09.200 is remote and also prioritize diversity, you will have twenty 0:20:09.240 --> 0:20:12.560 to thirty times more applicants. So I think that we're 0:20:12.600 --> 0:20:17.320 going to start seeing companies really investing in those two attributes, 0:20:17.920 --> 0:20:21.760 trying to keep as many jobs remote as possible, just 0:20:21.800 --> 0:20:26.199 because it attracts talent that companies are really struggling to 0:20:26.200 --> 0:20:29.800 find right now. And I think the level of automation 0:20:30.000 --> 0:20:33.919 is going to continue to increase, that will continue to 0:20:33.960 --> 0:20:36.679 increase an investment over the next five to ten years. 0:20:36.720 --> 0:20:39.160 In twenty years, I think we will all look back 0:20:39.200 --> 0:20:42.800 and say, why did we all do these crazy parts 0:20:42.920 --> 0:20:46.720 of our job? Why didn't we automate those It's because 0:20:46.760 --> 0:20:51.399 we were waiting for technology like Orchestrate provides. Do you 0:20:51.520 --> 0:20:56.080 have any specific advice for businesses that want to incorporate 0:20:56.119 --> 0:21:00.160 technology and automation in their in their business and their work. 0:21:00.560 --> 0:21:03.640 I would say, realize that you use automation every day, 0:21:03.920 --> 0:21:07.560 You use AI every day. So when you're using Google 0:21:07.600 --> 0:21:10.560 Maps or something like that, you're you're using your smartphone. 0:21:11.040 --> 0:21:14.360 You're accessing this kind of technology as a consumer, as 0:21:14.359 --> 0:21:17.879 an individual, there's no reason why you should worry about 0:21:17.920 --> 0:21:21.760 adopting it as a business, and don't feel intimidated by it. 0:21:21.920 --> 0:21:25.480 You are absolutely ready to use it and your business 0:21:25.520 --> 0:21:28.119 is ready to benefit from it. Just don't have that fear. 0:21:28.880 --> 0:21:31.760 We certainly is a company work with companies of all sizes. 0:21:31.880 --> 0:21:36.280 We have companies that have five to tend employees only, 0:21:36.320 --> 0:21:39.840 and we have some that have hundreds of thousands employees. 0:21:40.480 --> 0:21:42.960 That's a great thing about automation is it doesn't care 0:21:43.040 --> 0:21:44.920 the size of your company. It will work for you. 0:21:45.880 --> 0:21:48.040 Angela's fun to talk to you. Thank you for your time, 0:21:48.480 --> 0:21:51.320 congratulations and making it through to thirty And if you 0:21:51.320 --> 0:21:54.480 really think about that, that's a It's a really impressive 0:21:54.600 --> 0:21:57.600 level of persistence. Like I could imagine failing a few times, 0:21:57.600 --> 0:22:00.560 but I would have given up at nine or something 0:22:02.280 --> 0:22:06.080 at seven. At seven, I was like, I'm a crazy person. 0:22:10.359 --> 0:22:13.840 It is vitally important to get hiring right. What could 0:22:13.880 --> 0:22:18.200 be more essential to an organization's success than deciding which 0:22:18.280 --> 0:22:22.000 human beings make up that organization. If we let our 0:22:22.080 --> 0:22:27.480 biases go unchecked, we end up excluding qualified candidates, leaving 0:22:27.480 --> 0:22:31.919 our workforce is less diverse and therefore less competitive because 0:22:31.960 --> 0:22:35.399 of it. Angela made an interesting point earlier that I 0:22:35.440 --> 0:22:38.199 want to go back to. She said that bias is 0:22:38.200 --> 0:22:42.359 not a character flaw, it's a survival instinct, and that 0:22:42.440 --> 0:22:45.159 the best purpose technology can serve is to make us 0:22:45.240 --> 0:22:49.040 better humans by doing things for us that we can't. 0:22:49.880 --> 0:22:53.240 Bias is in human nature and we'll never truly get 0:22:53.320 --> 0:22:56.520 rid of it, but the first step to minimizing its 0:22:56.560 --> 0:23:00.520 impact is to acknowledge it's a problem we need help with. 0:23:01.640 --> 0:23:06.040 Intelligent automation can make hiring more efficient. When we allow 0:23:06.119 --> 0:23:10.480 computers to mitigate our biases, better hiring is the result. 0:23:11.240 --> 0:23:14.159 Sometimes to build the best team possible, we have to 0:23:14.200 --> 0:23:17.359 know when to listen to our human instincts and when 0:23:17.400 --> 0:23:21.560 to set them aside. On the next episode of Smart 0:23:21.600 --> 0:23:25.760 Talks with IBM, how to use data creatively in order 0:23:25.800 --> 0:23:29.920 to solve novel problems, We talk with YouTube content creator 0:23:30.160 --> 0:23:36.000 and IBM's senior Data science and AI technical specialist, Nicholas Renaud. 0:23:37.240 --> 0:23:40.480 Smart Talks with IBM is produced by Matt Romano, David 0:23:40.560 --> 0:23:45.920 jaw Royston Deserve and Edith Rousselo with Jacob Goldstein were 0:23:46.080 --> 0:23:50.679 edited by Sophie Crane. Are Engineers are Jason Gambrel, Sarah 0:23:50.720 --> 0:23:56.120 Bragair and Ben Tolliday. Theme song by Granmascope. Special thanks 0:23:56.200 --> 0:24:00.360 to Carlie mcglory, Andy Kelly, Kathy Callaghan and the eight 0:24:00.400 --> 0:24:04.600 Bar and IBM teams, as well as the Pushkin marketing team. 0:24:04.760 --> 0:24:07.480 Smart Talks with IBM is a production of Pushkin Industries 0:24:07.720 --> 0:24:11.720 and I Heart Media. To find more Pushkin podcasts, listen 0:24:11.800 --> 0:24:15.560 on the i Heart Radio app, Apple Podcasts, or wherever 0:24:16.000 --> 0:24:19.800 you listen to podcasts. I'm Malcolm Gladwell. This is a 0:24:19.880 --> 0:24:26.560 paid advertisement from IBM.