In this segment, we're going to illustrate some of the issues around data science has applied to managing people issues in the context of one very practical, really important problem. And that is hiring. So Matthew talked a little bit about the general context in which managing people decisions had been made and actions have been made before data science came along. We're going to drill down on this a little bit now in hiring. Why hiring? Because this is by far at least right now, the most important people management issue, it's the one on which most money is spent. The administrative costs alone of replacing somebody even the lowest level workers, about $4000. When you start looking at the costs of turnover and you move up higher in the organization, it's not uncommon to hear estimates that about two years salary is the equivalent cost of having to bring in a new person if you make a mistake and that person quits. A bad higher, that's really expensive. There's an industry about 200 billion dollar industry of helping companies fill positions. It's a really big business that we're talking about here. How is it supposed to work? Well, just as a reminder, most of you probably live through this. If you look at a textbook on how hiring works, they'll say you start with a job description, here's what will be required. Here is the kind of person we're looking for. Then you post and add some place, wait for people to apply. Once you get your applications, then you try to whittle them down to a short list. Short list you use more expensive whittling basically. Maybe you interview these people and give them tests, right? And then at the end of this, you make a hiring decision. The reality doesn't look at all like this now. So the census reports or data from the census reported last year that the majority of people who changed employers, we're not looking to move. They weren't searching for a job. Somebody came and got them or entice them to move. That process of creating a short list. These days That's done by applicant tracking software. So people aren't even touching the resumes anymore. It's automation on keywords. That's not, artificial intelligence is not even data science is just keyword tracking. Just looking to see in your resume whether you use the magic words in some cases, right? And because this is complicated a lot of people outsource it in their companies. So a company that may hire more people than anybody else in the US is a company you may not have heard of this company called People Scout. And they are recruitment process outsourcer they hire for other companies. They hire 300,000 people per year. Put that In perspective, the US army brings in maybe 120 000 new recruits every year. And People Scout is hiring 300,000. All right, so it's a big operation with a lot of scale, right? We look at how things are done right now. There's a real big push on passive candidates. That means let's see if we can find people who are not applying and go grab them and bring them into our system. The goal of most employers seems to be to get more people to apply to their positions. So, the way to think about this in most organizations, which by the way, I think it's a mistake. Come to this a second, is to think about this is a funnel. So we're trying to get lots and lots of people to apply at the very top. Then we'll screen them down with applicant tracking systems. And then from there we will try to use other screens to pull them down into the bottom of the funnel. So if 100 people apply at the very beginning here, at the top of our funnel, how many of those people are going to get job offers? Well, the evidence seems to suggest about 2%, right? So your odds on getting a job offer when you toss your application into an electronic hiring model, you go to a company and apply that way. Or you go to job boards like indeed and apply that way about a two chance that you will get a job offer. And the reason for that is because there are so many people applying for jobs, because the probability is low, we have people applying for 20 jobs, 30 jobs, 100 jobs. We made it easy for people to apply. And so that's what they do, right? So that's the reality here. And it's followed by an incredibly strange finding. And that is that when companies look to see how they're hiring, where they're doing a good job or bad job. Here's what they track Hong's it take for us to fill a position. Okay, how much do we spend to fill the position? What they don't track? So maybe only a quarter try to look at this is whether we make good hires or not. So think about this. We're measuring how expensive it is and how fast we can do it, but not whether we do a good job. So if you were to think about reviewing, let's say restaurants this way and the way you judge restaurants was fast and cheap. That Michelin star guide would really look different, right? If all you carried about is fast and cheap. So, when we start talking about hiring, one of the things to recognize is first, the way things are going now doesn't look like the textbook. And second for most employers, we have no idea whether we're doing a good job or not. So it's not going to be hard to do better than what we're doing right now, right? So let's talk about how we do better and how we apply data science to making hiring decisions. So there's two issues. Now, the first issue is applicants. And a lot of data science energy is going into trying to find those passive applicants. People who might look like the ones who are going to be good. And then the second, which gets even more attention is let's look at applicants and see if we can pick which ones are the ones we should offer jobs to, right? So how do we start? Well with data science, you start the same way you would have before data science came along. We're trying to figure out now what is a good higher. So rather than saying as we did before, well, let's look at what the job requires and then we're going to build an applicant tracking screening system or something like that. We say tell me who your best workers are. So the first thing we gotta do is figure out who's good and who's bad, how we're going to do that? Well, we got this data problem as well here. Maybe there's not a single good measure, but we're going to take a measure. And maybe it's performance appraisals, we'll use that one, right? And what we're going to do once we've defined what's good is we are going to try to identify what we know about those people? Where did they go to school? What kind of training did they have? If they had test scores, what were those test scores, like? The way we did it before is we relied on measures that mainly psychologists have said. We looked at these and these really do predict right? With data science, we don't care. Tell us everything you know about them, because we don't have to look at them one at a time, these attributes or characteristics. We're going to pile them all in together and build one model out of this, okay? So, once we identify everything about our best employees. There were going to say okay, now let's see if you can give me that information about you're not good employees because we need variation. Just as we're looking for ball bearings, we want to see the ones that break, but we want to see ones that don't break too, right? To figure out when they break what causes it. >> So we're trying to figure out here, what is associated with being a good employee and also what is associated with. Being a not good employee, the difference with data science is that we don't care what those measures look like. It could be just tell me whatever you know about the person, because we are completely agnostic about where the explanation. Is going to be driven by, as you know with a machine learning model and the algorithm that it produces. It is a really complicated, non linear, combination, of all kinds of attributes about that person and at the end of the day. It's going to produce a one number score forest, so the first thing we do is we look at our workforce and that data we've got. We cut it in half, we have the training data on which the machine learning software is going to learn, right? And that is it's going to build a model, that predicts the performance score that we're using to identify good versus bad workers, okay? And we're going to use the second set of the day to the second half of it to test it and see how well it does. Assuming it does a pretty good job, then what we're going to do is we're going to try to go to applicants and get their measures. On all those attributes ,that we included in our model of our own employees, where they went to school, how they did their. Where they worked before, where they lived, anything we think might be relevant that we used in our first model on the training data. To build the algorithm, we got a ask those same questions of our applicants and get all that same data, right? When we do that at the end of the process, what we get is a score for each candidate and the score is going to be. How well do they match onto our best performing employees? So you get a one number score, before this in the old days you might give people personality tests, you might give them IQ tests. Or dexterity test, three different tests, three different measures, they do interviews, you can interview score. Maybe there's something about their references, you get a score for those you get five scores and then you turn it over to. Some recruiter or some experts you hope, who's going to look across those five and make a decision. We moved to machine learning algorithms you get one number, and that number is the score and that's what you get, right? So what do we know about this model? It is likely to be much better at predicting, than anything that you are doing before because it's only got one goal. And that is to predict how well somebody is going to look like the people who perform well here before that's it, right? It doesn't have to be something which psychologists or economists or anybody else has said in the past predicts. All we care about is association, do these attributes that we're measuring and capturing for applicants. Those measures, are they associated with good performers? And if they are you get a good score, so what else do we need here? We need lots of data, we need thousands of applicants to build a machine learning model and that's going to be tricky. Unless you're a big employer, that's going to make an argument for trying to use vendors who might have access to lots and lots of. Applicants across many different companies, but there are issues there that are legal ones. That will come back to a little later here, okay? Now, let's talk about the question of bias because bias has gotten a lot of attention in the discussion of machine learning hiring. Here's the good news about algorithms that are generated by machine learning, the good news is they treat every candidate. The same, so for example if we're thinking about how much should a college degree matter and that's all we tell them is college degree. It's going to treat all college degrees the same, it's not going to treat college degrees for men differently, than from women. Recruiters, when they're looking at this stuff and their heads are full of bias their judgments are going to be full of bias as well. When you're using an algorithm, you don't get any of that bias, everybody is treated consistently. The bad news is that if there is bias in the training data that built the algorithm, there's going to be bias in the algorithm itself. It gets repeated, so the example that gets a lot of attention from this is that amazon built an algorithm to do its hiring. For it, and the motivation for this was perfectly reasonable one let's see if we can get better at hiring and also cheaper. Because if we get a good algorithm we don't need recruiters, we don't have to bother with this interviewing. It could be cheaper, faster and by the way may be better, but what amazon discovered when it started to use its algorithm. Is that when he looked at the scores it appeared that women were getting lower scores than otherwise equivalent men. Which was a puzzle, so what they did is they went back in and they took out anything that might identify the sex. Or the gender of the applicants, so it took out names for example, made sure that there was nothing that. Had a feminine masculine pronoun and it took out all that stuff, okay? And what they discovered was that the results didn't change very much, and the reason was because in the training data. Which was based on prior employees in amazon and current ones too, right? What was there was that men on average had gotten higher scores than women and maybe some of this was simply. Because there were more men in the data disproportionately there, but no doubt there was bias in promotion rates. In an overall performance scores as well, so what was happening the amazon algorithm was looking for anything. That might show up a relationship with gender, so if for example you have taken a women's studies course, right? It assumed okay, that's highly correlated with women, women do worse, you're going to get a lower score, right? So was the algorithm worse than what was happening without it? We will never know, but the difference was because you're using this algorithm and you're applying it to everybody. You can see the bias instantly, because you can look at those scores, look at them for men, look at them for women. See what appears to be different about them so for the same job, same education level, same other things that the courts and lawyers. Might care about and you'll see if there's a difference, so particularly if you're worried about getting sued. Here's the problem with using these algorithms, they may be better than what you were doing before. They may be less biased than what you were doing before, but you can see the bias easily and that's a problem, right? The other issues about this, if it's a good algorithm you can get rid of these applicant tracking systems. You don't have to bother trying to screen out people based on this measure or that measure. Just give them all to us and we'll just score them up, because it's quick and easy and give them a score, okay? You might also find. As some research has found as well, that some people who turn out to be really good fits with your job, don't have the attributes that you thought were important before. Right? So our colleague at Columbia, has done a study of this algorithm that was used for hiring. And one of the things he discovered, is that the algorithm was able to identify people who were good performers in jobs where they assumed you needed a college degree. Some of those people who got good scores, that is saying they're going to be good at this job, did not have college degrees anymore, right? Now, the problem with that though is if you only hire people with college degrees, you'll never know that. Right? So in order to really make use of these algorithms, you need to build them with data which doesn't begin with this selecting out of people already. Right? And in the future, in order to know that the algorithm continues to be good if for example, the population changes or the jobs change, you have to do some hiring that's random. That doesn't begin with screening out people and with job requirements that they must have this or this. Right? By the way, that's true in general of hiring. But with algorithms it becomes even more obvious that if you're screening people out, you'll never know whether you still need those screens and if they're still reasonable. Right? The other thing about algorithms, is that you can get some results which don't seem particularly intuitive. Right? So for example, years ago, there was a company that we had some dealings with here, which discovered in their applications that the zip code where people were living, was a pretty good predictor of turnover for call center jobs. Well, what was going on there, was really a measure of distance to the workplace. And these were jobs that didn't pay very well. And the farther the commuting distance was, the more likely you were to be absence more likely. Therefore, you were to ultimately turnover or quit or whatever it was because low wage jobs, you needed reliable transportation. Public transportation wasn't always so good, affording a really reliable car took maybe more money than many people had. So nobody ever thought to look at commuting distance before. And these folks discovered it. But then you see other vendors and other people making claims as well. So a well known one is a company that was claiming that facial expressions predicted your job performance. Well, how are they doing that? They were trying to map your facial expressions on to the facial expressions of their best employees in a company, and then giving applicants a score based on how closely your facial expressions mapped onto the facial expressions of your best performers. Okay? Well, how do you feel about that? Let's say it actually predicted, would you be okay with that? With a hiring model based on the facial expressions of your applicants? Would you be okay with that or not? Put this more carefully, this is something data science people referred to as explainability. Probably heard that before by now. Explainability means, can you tell somebody why this works in a way that seems sensible. If you had to go before a judge for example, and it turned out that your facial expression measure, gave worse scores to African Americans, because reading facial expressions with cameras might be more difficult to do in that context. Suppose that happened. And you had to explain to the judge how you were hiring, and you said based on facial expressions, would you feel comfortable doing that? Well, it's not clear that that actually did predict afterwards when you looked at those outcomes, not clear that actually does predict. But you might very well end up with some evidence suggesting at least initially that it does, right? One of the problems here is that there's thousands of vendors now, each one selling a hiring solution based on machine learning type algorithms. And so one of the things you want to be very careful about in dealing with vendors and whatever their claims are, is ask them, can you show me the evidence that this works based on real job performance? I understand how you built the algorithm, but afterwards when you tested it and you hired people are selected people based on it, did those people really perform well? And the final thing to remember about this, is if you really want to be able to defend your hiring practices particularly against lawsuits, that your criteria has an adverse impact against women or minorities or virtually any other protected group which includes everybody. Can you do it with your own data? If you can't show it with your own data that this algorithm really predicts, then if it turns out that it has some adverse impact against people in any of those categories which is basically everybody, you're in trouble. If a vendor tells you that it's valid that it predicts that's fine. But it doesn't help you to demonstrate that your own hiring practices are good ones. So one of things you have to think about, you can't just buy these things off the shelf, even if a vendor can persuade you that their data looks good there evidence shows it works. You have to be able to test it on your own data and do that. So it's a complicated set of issues here when we start thinking about applying this to hiring, which is where most of the action is right now.