Siri Chilazi - Making Workplaces Fair by Design
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Hello and welcome to the HR Data Labs podcast. I'm your host, David Teretsky. Like always, we try and find the most brilliant people inside and outside the world of HR to talk to you about what's going on in the world today. Today I have with us a repeat guest, one of my favorite guests, Siri Chalazi. Siri, how are you? David, thank you for having me. It's so lovely to be back. You are one of my favorite podcast hosts. Oh, thank you. What's funny is, is that every time I mention your name...

The phone, my iPhone goes, what do you want? You called me. I've been told that happens. All I can say is I'm sorry. Well, no, you don't have to apologize. It's just hysterical though. So has that caused problems as you've grown with it? I would say on balance, it's made my life easier, to be honest. People remember the name. They know how to spell it. It's easy to make reservations over the phone. So I'm not going to be one to complain. Not at all. There you go. So.

So, Siri, tell people a little bit about yourself. I am a researcher of gender equality in organizations at the Women in Public Policy Program at Harvard Kennedy School. So my life's work is to try to identify and test concrete solutions to help close gender gaps at work and to help make the workplace more fair for everybody.

That means things like how do we hire better? How do we make better decisions around performance evaluations? Who to put on a particular project? Who should be fast-tracked for future leadership positions? And also things like organizational policies around flexible work, remote work, parental leave, and even expense reimbursement policies. Wow.

So to kind of summarize, everybody should be calling you. Your phone should be ringing off the hook because everybody's trying to figure all those things out right now. Yes. These insights that we're generating in academia are relevant to every single person in the workplace, whether you're the CEO, the head of HR, a mid-level manager with a team of five people reporting up to you, or the most junior person who was just hired a month ago. Right.

There is something that social science and behavioral science can help you with in terms of making your own work more fair. And my big passion is not only generating these insights, but also making sure that they reach the people like all of you who are listening right now who can actually use these evidence-based insights to work better.

So is that the reason why a lot of companies are actually hiring anthropologists lately to kind of understand and connect with people better? That's interesting. I didn't know about that trend, but I wouldn't be surprised. I think we're realizing that the more perspectives we bring to bear from different disciplines, right? Because depending on where you come from, where you sit, you have a different way of looking at problems. Right.

We're going to get better insights and be able to generate better decisions and have better solutions when we bring in this diversity of perspectives together. Well, this podcast is all about getting better decisions made. So thank you so much for being here. And by the way, what I'd really love it is you can provide us with the links to your research so that people can know how to follow you and know how to get access to your research. For sure. Awesome.

So, Siri, you've been here before. You know, what's one fun thing that no one knows about you? See, now you're asking me to come up with the second one. So here goes. I hate dressing up for Halloween. I know I'm never supposed to admit this publicly, but if forced to do it, I have made life easier for myself. This is a classic behavioral science hack by having a go-to costume. And my go-to costume is Elle Woods from Legally Blonde.

Yes, you can pull that off. I have a pink suit, you know, hanging in my closet. I have the mini version of her dog bruiser. And off I go in my little purse, my little mini bruiser and my pink suit. That's great. And by the way, one of the fun things about her character, she's a brilliant person. But she hides behind the facade.

of being this blonde, but yet she's so brilliant and it comes across in the things she accomplishes. Yeah. Well, and another way of framing that is to say she's being herself, but because she doesn't, her exterior isn't a traditional package that we're used to seeing brilliance in. The world has trouble seeing her brilliance. Exactly.

They don't take her as seriously as they really should. Just because she's a blonde in a pink suit. Yeah, exactly. Which is just ridiculous. So anyway, so that's good to know. And so next Halloween, we want to see those LinkedIn pictures about that costume. The go-to. I'm making no promises. Oh, okay. So Siri, one of the fun things we get to do is the topics that we talk to

to people about. And a lot of times they're perfect from their background. And today's is going to be another one of those great discussions. We're going to be talking about how we can make it all our own work, our workplaces. We can make fair through smart evidence-based behavioral designs. So

So Siri, our first question, in your book with Iris Bonnet, Make Work Fair, Data-Driven Design for Results, you propose a paradigm shift for how we think about fairness at work. What is this paradigm shift and why is it important for organizations and practitioners to actually engage in that?

Here's the paradigm shift. Fairness is not a program, but a way of doing things. Corporate DEI has been very popular over the last five, 10 years. And while well-intentioned, it's often been focused on programmatic solutions that are separated from the daily flow of work. So it's go to this training, go to this employee resource group meeting, go to a networking event. Let's send you to a different training course now.

And first of all, the problem with that is most of those interventions and solutions are not evidence-based. Diversity trainings and unconscious bias trainings in particular have been studied extensively and have not been shown to really lead to behavior change. So that's already one problem. But then the other problem is that because a lot of these solutions are programmatic,

When people get busy, when budgets get cut, when political climates and winds shift. And have. And have. It's too easy to cut all of these line items. So the paradigm shift that Iris and I propose in our book, Make Work Fair, is to instead embed fairness into the things that we're already doing, no matter who sits in the White House, no matter what the macroeconomic conditions look like. We're going to be sitting in meetings. We're going to be producing communications.

Our performance is going to be evaluated. We might be involved in hiring new colleagues. Senior leaders and organizations will identify certain people to be fast-tracked for future high-potential projects and potential leadership positions. These are all things that are happening day in and day out in organizations. It's those daily work systems and processes that we can tweak

Small tweaks, simple tweaks, often low-cost or low-cost tweaks to help make the workplace a more fair place for us all. But this takes a mindset shift then, right?

for how that decision gets made and how does that get communicated how does that get introduced if it's not going to be programmatic and it's not going to be through taking people to a course or reading a pamphlet or whatever what do they have to you know what kind of mind tricks are we going to play on them jedi or otherwise in order to be able to get them to kind of refocus themselves on being more fair

So this is the interesting thing, one of the most interesting insights that behavioral science has to offer to us. And that is that we can shift people's behavior without shifting their mindsets, without shifting their underlying attitudes. So let me give you an example from recruitment. I was only six months into my first job in management consulting straight out of college when I was first invited to be part of the recruitment team for summer interns for our firm.

I got minimal training. I largely had a lot of leeway and flexibility in how I was going to conduct my interviews. They weren't gold standard because actually science tells us the more structure we can put into the process. Things like predetermining the questions in advance, asking every candidate for a particular role, the same set of questions in the same order, having a predetermined grading scale, grading as we go through. These are some of the evidence-based best practices.

If I had known then what I know now, I would have used those best practices to change how I conduct interviews. And I might have shared some of those approaches with my colleagues. I didn't have power at the time to change hiring practices for the whole firm, but I could have said, hey, I read this book or this study that suggests to make the best, most objective decisions. Here's how we should conduct our interviews. I'm going to be doing it this way and you might want to follow suit. Right.

So this is how you can make your work fair, even if you are literally the most junior person on the team. Now, going back to your question about the mindset shift, if you are a more senior person, a senior business leader, the head of HR, you could actually make that change for the whole organization in one fell swoop. And you don't need to change people's mindsets to do that. You can simply say, hey, we're moving to a new way of conducting interviews because research shows that this is

much better, allows us much better to identify the best talent. So going forward, we will do A, B, C, and D. People's mindsets don't have to change for them to follow the new process.

No, in fact, they actually want process. They want to be led a little bit because in the absence of being led, they usually do terrible jobs, especially at interviewing. Yes. And then they blame it on HR. Well, you know, I didn't find the right candidate because they didn't give me a guide to, you know, ask the right questions. And I wasn't given a scoring sheet. And I just use my gut. My gut's usually right. Yeah.

Exactly. And the ironic thing is most companies today will say that their people are their greatest asset.

And if that's actually true and if that's what we believe, then identifying the right people for the right roles should be one of our highest priorities, which means we should be wanting to spend more time on it. We should be wanting to be more deliberate about ironing out and articulating who is it that we're looking for? What are the skills, the competencies that we need?

Instead, what we often see is people are super pressed for time and they're recycling and minimally editing and updating old job descriptions from 10 years ago. Not a recipe for success.

No, on either side, right? Because you want to be able to make sure it's the right fit for you as a candidate. And you also want it to be the right fit as the person coming in because trying to unwind bad situations, bad hires is usually pretty terrible. And it's very frustrating and it's very emotional on both sides. Yes, it's costly both in the traditional financial sense, but to your point, it's also very costly emotionally and relationally.

So we should be trying to avoid hiring mistakes as much as possible. And process helps us do that. Absolutely. And I love the idea of being able to provide people with those guides and, you know, have a have a good process and have fairness be embedded in it. And to kind of go back to one of the other points you made, because DE&I has completely fallen out of favor.

But the concepts and the principles of DE&I hopefully have changed people's mindsets about being fair and being able to provide opportunities to the right people at the right time. And if that gets crept in, whichever way it does, whether it's through a process or through people going, yeah, I'm going to see a good diverse slate of candidates. Why? Because it's probably the best thing, not because someone's forcing me to do it.

And in that case, you're kind of advocating for, in terms of the word fairness, I imagine you're advocating for those principles to be laid down within those processes, I imagine. That's exactly correct.

I have yet to encounter a business leader or, frankly, a person in an organization who opposes the concept of fairness. Now, granted, some people define it slightly differently. Some people might have slightly different understandings. But when I ask senior leaders point blank, do you care about running a fair organization? To the last person, they all say yes.

Yeah, I can't imagine that kind of conversation, how that would go. Do you believe in fairness? No. Huh? But that's where we need to start, right? We need to start by finding the common ground. Once we have that, and once we can all agree that this is a principle and a vision that we can get behind, then we can get into the nitty gritty of how do we actualize this vision? What does it look like to actually have a fair organization? Right.

How do you measure that though, Siri? How do you measure what is fairness and how do you measure that fairness is actually taking hold? Thankfully, we have data. This is where it goes back to data, data, data, data, and measurement. Let me give you an example. Representation is obviously something obvious. One thing I would want all organizations to look at is the representation at all different levels and how those levels relate to each other. So if you're

entry-level employee base is 50-50 women and men, let's say, then it's kind of hard to justify that only 25% or 30% of managers are women because your pool is already there. You've got a 50-50 pool. So what happens in those couple of years when employees mature from entry-level to management that we start disproportionately selecting for men? That to me would be a signal that

or a flag to dive deeper into the company's career development processes, performance evaluations, how do promotion decisions get made, and to assess those processes and check if there's some unconscious bias creeping in unintentionally.

So data can help us do that kind of analysis. Data can also help us, for example, identify whether people have an equal opportunity to actually move up to the levels of an organization. One data point that I find very interesting is time in role before promotion. Right. So let's say we're looking at the manager level and in this hypothetical organization of ours, the next level up is director. Right.

Let's look at all different groups of employees and see how long they spend as a manager on average before they get promoted to director. And of course, not everyone will. Some people will exit. Some people will stay as manager. But regardless, that will tell us, does everyone actually have a same shot to succeed? Because if disproportionately certain groups of people are advancing through the hierarchy faster, that's not going to help.

That might be, again, a flag that they're getting evaluated differently. They're getting more support along the way. They're getting more access to senior leadership visibility or high potential, sexy, high visibility projects. Right, right. Well, that part I would imagine would be

harder to measure. But I also want to challenge one quick thing on this, and that's the numbers we're talking about in terms of the promotions is going to be very few on a yearly basis. But if you look at history, you might be able to be a little bit more clear because we know that if you're looking at just two or three promotions, that's not really going to tell you anything. It's not really a trend.

But if you're looking at 50, 60, 70 promotions over time, and I'm thinking more about in smaller midsize companies, it's going to be harder to measure. In large, large, large companies, it is because that promotion is happening quite readily. How do we address that, though? Because we're not going to be able to see a lot of patterns unless we dive into probably more data, longer periods of time. Absolutely true. Absolutely.

On the flip side, though, if you have a small organization that's going to be a small organization, the numbers still matter. Yes, they are highly susceptible to fluctuations because one person can move the percentage, you know, up or down by double digits, double digits percent.

But it really still matters who you're promoting and who makes up the senior leadership, even if it's just three people. So in a way, you could argue that in smaller organizations, all of those individual decisions are actually even more consequential, precisely because the numbers involved are so small.

Oh, absolutely. I wasn't challenging that. In fact, it is much more apparent and much more visible when it's small numbers. Totally agree. I'm just saying that if you're in a smaller midsize company, the measurement of them will be more difficult to try and draw patterns and trends over time, unless you're dealing with much larger trends over time than just looking at a point in time. Yeah. Your data won't be statistically significant, but it's the data you have, and so you do the best work.

looking at it. One of the things that I always find in talking with individuals and organizations is often people are surprised by what the data shows.

collecting systematic data, whether that's of five people, if that's the size of your organization, or 500,000, if that's the size of your organization, it often yields surprises. Our perception of reality is not always what data reveals the reality to be. And that's one important function of data is it helps align our understandings and perceptions to what is truly going on on the ground.

Right. And perception equals reality for people who are the ones who are turned aside or the ones who are not promoted or the ones who aren't even considered. Yes. And we need to address them. We need to be able to pay attention to them because we certainly don't want to mitigate them. We certainly don't want to minimize their emotional aspect of this. Yeah.

Like what you hear so far? Make sure you never miss a show by clicking subscribe. This podcast is made possible by Salary.com. Now back to the show. How do we, though, try and draw? Because you're saying that this is not programmatic, right? This is going to happen because we're changing process. How do we change process to make progress and to recognize these issues?

Let me give you an example from the world of hiring. This is one of my favorite studies. A lot of employers don't like to see gaps in job candidates' work history. They assume that it means candidates were not committed, they were sick or taking care of a child or a family member, or maybe even pursuing continuing education or taking a sabbatical and traveling the world. But in any case, it's not good because for whatever reason, employers want you to be working 24-7 all the time. Right.

So we know this from past research that career gaps are penalized.

The typical resume format makes career gaps really salient because you list your job that you had and then the exact associated dates. From 2020 to 2024, I was here. Siri, can you explain why you have gaps of nine months and 12 months in your career? Exactly. Well, you're lucky if you get to that stage, by the way. In most cases, you won't even be invited to explain those gaps. You'll just be discarded in the first layer of review. Exactly, yeah. So...

My colleagues, Ariella Christel, Oliver Hauser, and colleagues tested a slightly tweaked resume format, which retained all the information, but instead of listing job experience with the dates attached, it just expressed it in amount of time. So three years, roll X, four years, roll Y, and so forth. I like that. You don't lose any information except potentially the gaps. And

And they found that this redesigned way of presenting work experience actually increased everybody's chances of getting invited to an interview. But it, of course, disproportionately helped women who today are the applicants most likely to have career gaps. Of course.

Because of the fact that women still do the most of unpaid caretaking work, whether that's kids or elder care. So that's an example of how we make a small tweak to a process without trying to tackle people's underlying biases, which in this case are biases against candidates with career gaps.

And we get access to a bigger talent pool, which then allows us to make a better hire for the job that we have open. And I think people who are older as well might face some gaps as well. Yes. Male, female, ethnicity aside, that's a phenomenal way of being able to point out that you have the experience and you have experiences in various pieces at various companies that

But by the way, I'm 57 and there's no sugarcoating when you put in that you're that you graduated college in 1986 or sorry, 1989 that you're you're old. So so I love that idea of having the format change be representative for everybody representative of your experience. I think that's wonderful.

Yeah. You're highlighting something else, too, that's really important, which is the move towards skills-based hiring. So instead of looking at people's check-the-box qualifications, which university, which degree, you know, how long were you in school for? How long did you work at a particular job? We should really be looking at what are the actual skills and competencies that people bring with them?

There's any number of ways to gain, let's say, coding experience. One of them is to go to college and get a degree in computer science. That's great. Another one is to grow up in a family with lots of computers and, let's say, older siblings who are teaching you to code at age five, and you're a pro before you even get to college age. So if at the end of the day what we need are people who know how to code, we don't actually necessarily need people with computer science degrees.

But that specific result, though, that specific outcome is very difficult for people to wrap their head around. Because even when you're talking to HR people, the concept of competencies and skills and even the assessment of them is a very, very different concept.

And I've been doing a lot of consulting around job descriptions and how do we actually describe work in the concept of skills. And people just have a very hard time. They're very straightforward when it comes to duties, right? Yes. What are the duties? What are the responsibilities? And you say, what skills do they need? Oh, yeah. Yeah.

What do you mean? Yeah. So Siri, how do you get people's minds around changing from here's what I accomplished there and here's what I did to here are the skills I used and here are the outcomes that that led to? Yeah. This goes back to our conversation earlier about if we really think our people are our greatest asset and finding the best people for these jobs is so important.

then we should be willing to spend a little bit more time to start from a blank page, sit down and say, what are actually the skills and competencies that will make this new hire successful in this role? And that is an effort. I'm not going to lie. You know, I just did this recently for a job description myself, and it took me a lot longer than I expected, but it was time well spent. It is a mind shift. It's a very different way of thinking about your job or thinking about a job.

And it needs, I don't know if it's necessarily just technology, but the way you put also the change in the resume, it needs that kind of a shift. It could be subtle. It could be very subtle. How do you get your job done? What are the things you do in your job? Oh, well, I do a lot of selling, but it's very consultative. That's a skill. That's somebody being able to illustrate how do I get my job done in a way in which takes skills.

And people just, I mean, it's very difficult to get that out of people, especially with people who have, and actually I take it back. I think people who are not in HR have an easier time of expressing that than people in HR. Well, and this brings us to an interesting question, right? Which is, should we rethink who writes job descriptions and how they get written?

If you, David, are doing your job over here, some HR person that you've never even met, how could they know how to describe your job? So maybe this needs to be a more collaborative effort. This would bring more of a diversity of perspectives to the table, which we know from research is always a good thing, right? So get the people on the front lines involved. Get them to describe what are the critical success factors. To be honest, we used to do that. We used to do what was called a position description questionnaire. Right.

And people used to fill out what did they do, how did they do it, how much time did they spend, it was a percentage. And we used to gather those up and then we used to go speak to the supervisors and say, is this right? Do you think it's correct? And then we'd synthesize that into one and that became the job description. Right.

Yeah, but that's too costly. And so that went away. And then other people got to write that description for them. And sometimes it was the manager, which was good because they should have an understanding. But to your point, because that manager is not doing the job on a daily basis, it's usually inaccurate. Yeah. And this is some interesting intersections with the concept of employee voice.

The concept of inclusion, right? Soliciting people's views, perspectives, experiences around the jobs that they're actually doing makes them feel more valued in the organization. When the organization says, hey, I actually care about what you do. If you're getting promoted and we need to hire someone to replace you, we actually want to get your perspective on how to select the best person. That's only going to help build more loyalty. Yeah.

I'll tell you another example of where this is important. And I faced a lot of organizations that say we want to create a career hierarchy. We want to create a career ladder for these individuals in this job. And they're not sure what differentiates a level one, two and three in the job.

To your point, it would be best to ask those people, not what do you do, but what skills do you use for each of those individual roles? Then what you can do is the people who you think of as a one, look at the commonality in response. People in two, people in three, and they have described what their competencies are and what behavioral levels...

we should expect of those people. Yes. And from a design perspective, I would also challenge whether we need three levels.

Right. Who decided that it has to be three? Maybe it's just one. Well, it's always the rule of three, Siri. Or two. Well, we always do the junior, intermediate, senior. It just feels comfortable. That's the way we've always designed job architectures so that, you know, we can try and fit. Oh, come on, Siri. Everybody likes to put people in boxes, right? They like to say, you're this, you're that. And, you know, we try and have these paradigms like senior, junior, intermediate, but...

But it just works out that way. Yes, you're right. Should there be differences? Of course. Like, look at the investment banking world. They usually have analyst programs and associate programs. People go through the program. You go, you know, you graduate college, you go through the analyst program, then you go back to school and you become an associate after you've graduated grad school and you go through that process.

We love trying to put people in those little, you know, cubby holes, those boxes. It makes sense to us. And then we can compare them and we can measure them. Doesn't always work out that way. I guess the bigger point that I'm trying to make is always doing things the way they've been done can be an enemy of continuous improvement.

And part of the work that we need to be doing, all of us individually, as well as organizational leaders, certainly from the remit that they have, is to question some of these current practices and say, just because we've been doing it this way forever, it doesn't mean that it's the best way. Might there be a more objective, a more fair, smarter way to design our work? My favorite question is a three-letter word. Why? Why? Why do we do it that way?

Why have you always done it that way? What makes that the best process? And usually people have no idea. They usually don't know because it was not the person before them that did it, that designed it or the person before them. It was someone, some amorphous person who has no name. Yes. And that's what they've stuck with all these years. Yeah. It,

It's human nature. Status quo bias is real. We tend to have an inherent preference for the way things are just because it's the way things are. But once we become conscious of that tendency, once we know that that's an inherent way we like to operate, then we can question that when needed. Hey, are you listening to this and thinking to yourself, man, I wish I could talk to David about this? Well, you're in luck. We have a special offer for listeners of the HR Data Labs podcast.

a free half hour call with me about any of the topics we cover on the podcast or whatever is on your mind. Go to salary.com forward slash HRDL consulting to schedule your free 30 minute call today. So I know we've covered this. And so maybe this question is kind of inappropriate now, but how do we get smarter about the data that drives fairness? I mean, we just talked a lot about it, but is there anything else we can do to kind of

One of my favorite stories about data comes from the world of the media. Ross Atkins, he is a British journalist and TV presenter at the BBC. And he realized several years ago that he had no data available to him.

on the number of women and men he was featuring as experts on his nightly primetime news show. He had the strong conviction that because the world he's reporting on is approximately 50-50, the journalism should be as well in order to accurately represent the world. But he didn't have any data to tell him whether he was reaching that goal.

So he and his team decided to generate the data themselves and count at the end of each night's one hour long show. Okay, who did we have on screen during this hour? How many women? How many men?

And unsurprisingly, they discovered that they weren't doing quite as well as they thought at first. Women made up only 39% of all the contributors. Wow. So the data, number one, helped them understand the current state of the world so that they could then make a conscious change if they wanted to. They did. They set themselves a goal of getting to 50-50.

And within four months, they got there. And then they stayed there for years and years until the program's conclusion. But one of the other aspects of this story that I love that it illustrates is data is powerful when you are so close to it that you can actually use it to shape your everyday decisions.

Oftentimes what happens in organizations is there's an HR or a people analytics team or some statisticians, you know, PhD data scientists, statisticians that are collecting and analyzing data over in a silo. And the people who are making everyday decisions about performance evaluation scores, whom to hire, whom to promote, whom to fire, don't have the data anywhere close. Right. So bridging that gap.

and allowing the people whose daily decisions shape what the data will look like, giving them access to the data so that they know at any given moment where they stand and how they're doing, that's critical. And I think that's one way organizations can get a lot smarter about integrating data into everyday work. I love that idea. The problem that I faced in the past with this though, Siri, is people that are not in HR look at this as an HR dashboard,

which I hate the terminology. And it's data that are statistics that don't really tell them anything because they're focused on really transactional things like what's my head count? How many hours did my people spend on PTO last week?

They're not really thinking about the outcomes that we're trying to generate. They're not really thinking about the real impactful statistics that are going to drive better business decisions from these managers. So if you had advice for the people who are listening, what would you tell them about the statistics that are really more meaningful to those people?

My advice would be if you start with a mountain of different metrics, you're going to get lost almost every time. You're going to get lost in the weeds of so many data points. So start instead, to your point, Dave, start with your aspirations and your goals. What are we actually trying to achieve here? In Roz Atkins' case, it was journalism that represented women and men equally.

From that then, he was able to focus on exactly the data point that he needed to collect and analyze and keep tracking in order to reach that goal. And for HR folks listening who maybe have control over which data points to share broadly with employees, I would give the same advice is keep

Not that we want to hide things, but sometimes if you share too much and data points that aren't really relevant to the behaviors that we're trying to drive, it can be really easy for people to get lost. So let's keep our eye on the ball and share and focus on the data that relates to that particular ball. I just want to share one thing with you. I've actually just read recently something that said, listen to your people, listen to what they're asking for.

And then listen between the lines of what they're asking for to try and come up with what are the insights that you think you have access to that they are really asking for? You know, if they say, come on, could you send me a table of the people on my team and what their service dates are?

Maybe what they're looking for is, to your point before, when's the last time someone was promoted? Or what are the experience levels of the people on my team? And then try and work with them to ask them the right questions to be able to really kind of get out of them. What are they really asking for? What's the better metric to provide them to get them to where they're going? Yes. And you also touched on something a moment ago, which is often we share data at such a high level of aggregation that

that it's so divorced from people's own everyday experiences that it's not helpful anymore. So to an individual manager, they need the data pertaining to their team and maybe comparing their team to other peer teams. Do they need to see the rolled up data for thousands of people across the whole company and in overseas offices that have nothing to do with their daily work? Probably not. So we need to make the data that we're providing people actually helpful and relevant to them.

line of sight, if it matters to them, it's going to be more impactful to them. 100%.

Siri, it's always a pleasure to talk to you. Likewise. We were talking before about how it's crazy that the time of the year just flies by. Well, same thing on these recordings. It just flies by when you're talking to someone so brilliant and they just get it and you love it. I love speaking to you. Thank you. We're going to have to have you back. It's going to have to be, you know, less than three years, though, next time. Yeah.

Well, that's a promise. Okay, cool. Siri, thank you so much for being here. We really appreciate you being on the podcast. Thank you so much for having me. And thanks, everybody, for listening. And of course, Siri, my Siri, just had to say something given the fact that I was speaking to the other Siri. Again, take care. And everybody, thank you very much and stay safe.

That was the HR Data Labs podcast. If you liked the episode, please subscribe. And if you know anyone that might like to hear it, please send it their way. Thank you for joining us this week and stay tuned for our next episode. Stay safe.