So thank you for having me.

Absolutely. It's our pleasure. And we can see your slides, and I will leave the stage to you.

Fantastic. Thank you, Anna. So as Anna mentioned in that wonderful introduction, I'm thrilled to share with you today some actionable solutions that all of us can implement, whether we are the CEO or the most junior member of the team to build fairness into the workplace. And that's actually exactly what Astrid Linder did, and I'd like to introduce you to her now. She's here in the picture on the left. Astrid is a Swedish mechanical engineer, and she also serves as the research director for Sweden's National Highway Safety Administration. And she was very familiar with the data that shows that even though women around the world drive slightly less than men, women are actually more likely than men to be injured in car crashes, especially in rear impact collisions. And Astrid also knew why that happens in large part because even today, cars are still only safety tested on men.

When cars are manufactured, they need to go through safety testing as part of which we have these human like dummies in the car that allow us to develop better safety features. And regulations around the world only require those dummies to represent a man's body. And it turns out there's some important biological differences between an average woman's body and an average man's height, weight, torso shape, body mass distribution, even joint laxity. And because of those physical differences and because of the fact that cars are primarily designed to keep men safe, women are more likely to be injured. Now in her role, Astra didn't have the power to change regulations around the world regarding car safety testing. But what she could do along with her team of engineers and what she did do is she developed the world's female crash test dummy modeled on an average woman's body.

And in fact, she has developed not only one but several dummies, and she's inspired other research teams and car manufacturers around the world to start creating their own dummies. So we now have a small woman, a big woman, a pregnant woman, all these different versions of the female body that cars are starting to be safety tested on. And there's even movement on the regulatory front. Regulators in the European Union, in The United States where I'm a bay where I'm based, and many other countries around the world are starting to require that cars are actually safety tested on all the different types of bodies that occupy them. This is the essence of making work fair. It's about doing your job, all the things that you're already doing in a slightly better way so as to make sure that everyone, in Astrid Linder's case, stays better protected and safer in cars.

Or if you are a marketing intern, that the pictures and videos that you select to represent your company are actually reflective of your employee base or the market and the consumers that you seek to serve. Or if we're hiring people, promoting them, deciding on who gets promoted. Making work fair means that we make those decisions in the most objective way possible so that we're actually identifying the best talent and promoting the best people or selecting the best people. So I'd like to share with you a very concrete framework for how we all can make our own work more fair, and in so doing to make our organizations as a whole more fair. And that entails making fairness count, making fairness stick, and making fairness normal. The step making fairness count is about approaching fairness in exactly the same way that we approach our core business using data driven tools like collecting metrics, setting goals, implementing incentives to help us actually deliver on those goals, and then transparently tracking data and monitoring progress to create a loop of accountability so that we know if we're actually on the right track.

And this is exactly what Roz Adkins did at the BBC. Roz is here in the picture on the left. He's a career journalist at the BBC based in London, and he cares very deeply about the quality and representativeness of his journalism. But Ras realized a few years ago that he didn't actually have any data on hand to know how high quality his journalism was and whether it was reflective of the world that he is reporting on. So Ras invited his team to join him in a very simple counting exercise. At the time, Ras was the presenter or the anchor of a nightly prime time news show. So every single night after their one hour on air, the team would take out a pen and a Post it note and count how many people did we feature on screen during our sixty minutes and how many of those people were women and how many were men.

As I mentioned, they counted after every night show, and after one month, they averaged out the results and discovered that only 39 of the contributors that they had selected, the people that they had brought in to comment on the day's news were women. It was a lot lower than what they were expecting. So Ross and his team set themselves a goal of getting to fifty fifty because the world that they're reporting on is roughly fifty fifty women and men. And then they started getting a lot more intentional and deliberate about how to select contributors. Their golden rule was still that the best person, the best expert would always go on air, but they realized that they tended to have go to people, like the one guy they always called for any aviation related news stories and the one guy they always called for any China related events.

And it, of course, occurred to them that there are more people in the world who have interesting perspectives to offer on aviation and China related issues. So that was one of the key ways in which they were able to diversify the range of voices is just tapping a broader range of experts. And within a few months, they reached fifty fifty, their goal, which they kept up for years. But then they did something really remarkable, which is once they'd proved the concept of the simple data driven counting methodology, they started sharing it with their colleagues at the BBC. And Rob and his team weren't in an institutional position of power, so they didn't have the authority to compel people to join them. They also didn't have a budget to incentivize people to jump on board. Instead, they simply appeal to journalists' common motivation and said, listen.

We know you're a busy journalist just like us. We get it. The nature of news is uncertain and super fast paced, but we only spend two minutes on this a day, and it's really helped us to improve the quality of their your journalism. So you might find value in this as well. And long story short, about eight years later as we fast forward to today, what's become now known as fifty fifty, the equality project, has spread to more than 750 content creating teams within the BBC, as well as more than a 150 external partner organizations, including journalism schools, other media companies, and even corporations.

And audiences have noticed too. The BBC regularly conducts audience surveys, and these surveys have revealed that all of their viewers have noticed the increasing diversity in content. And women in particular report enjoying the content more and have been shown to actually consume more of the content. So this was not just a fair and right thing to do, but it was also a smart thing to do from a business perspective to more accurately reflect the world as it is, to make journalism more fair. That's one example how all of us, regardless of our seniority, can make fairness count in our own work. Here's another example for more of a corporate context from Google, which is a company that we are all, of course, familiar with. Google was looking at its employee data a few years ago and discovered what looked like a gender gap in retention.

It appeared that women were more likely than men to leave the firm. But because Google collects a lot of detailed data on its workforce, when it did a deeper analysis, it was actually able to pinpoint the scope of the problem specifically to new parents. It was actually women who had recently become mothers that were the single group that were most likely to leave the company. So because data had allowed Google to accurately diagnose the root problem, it was then able to test a targeted solution, which in their case was increasing the amount of parental leave offered to all employees, not just women, from twelve weeks to eighteen weeks. And in fact, Google has since extended the amount of time offered even more to twenty four weeks. And as they continue to analyze the data, they were then happily able to discover that the solution in fact worked to close the gap in retention.

That's another example of how data can play a critical role in helping us to make fairness count, in helping us to accurately diagnose where we have problems, and then track whether the potential solutions that we're implementing are actually working as intended. So once we've made fairness count, we then have the opportunity to make it stick. And this is all about making small tweaks to our existing ways of working, our current processes and systems, in order to embed fairness into them so that we don't have to remember or make an extra effort on a daily basis to behave on to follow through on our virtuous intentions, behave in the ways that we hope to to uphold fairness.

Here's an example that probably will resonate with everyone on the call today because my guess is you all have a resume or a CV. And my guess is most of your CVs are formatted in the traditional way where you list your past experience along with the specific dates. So in this case, we have a fictional gentleman, Robert Matthews, and he was a junior project manager from 2007 to 2019. Well, one of the things that this resume format makes very obvious is any potential career gaps. So in Robert's case, he was a project assistant until the year 02/2003, and then his next role starts in 02/2007. In this format, it's very obvious that there's a four year gap, and data continues to show that employers penalize candidates with these gaps.

People with non continuous work histories for any reason, whether that's taking care of children, elderly parents, taking a sabbatical, taking time off to care for yourself if you have a medical issue. Maybe you worked on a start up that didn't pan out, you now don't wanna mention on your resume. Whatever the reason why you have a gap, you are simply less likely to get hired with that gap on your resume at least today. So there's real discrimination against people with non continuous work histories. Our colleagues, researchers in The UK, ran an experiment to test a solution to this problem, And they ran an experiment where some people sent out the conventional version of the resume with specific dates attached, and then identical resumes were sent out with a slight reformat.

So instead of listing the dates, we just listed the total amount of time. In this case, four years as a project assistant or twelve years as a junior project manager. And, of course, in this reframe, we don't lose any information that's relevant to assessing a candidate's skills or competencies because their experience is still there. But what we do do is we obscure the career gaps. And in fact, was this study found more than 9,000 companies hiring for a variety of role rate roles ranging from financial analyst and computer software engineer to call center worker and care worker. What they found was that both women and men's likelihood of advancing in the hiring process, moving to the next stage of the interview, increased by 15%.

The employers were not distracted by career gaps, but they were instead able to focus on the skills that candidates actually brought to the table and whether those skills were a good match for the roles that they were hiring for. That's an example of making a small tweak to an existing process design, in this case, the formatting that we use on our CVs, on our resumes, to make evaluations more fair. Here is another example from a study that my colleagues and I worked on with a large telecommunications and engineering firm with more than a 100,000 employees in more than a 100 countries around the world. This company, like us, knows that there's been a lot of research done on diversity trainings and unconscious bias trainings, and they have consistently been found not to be helpful at changing people's behaviors. And yet this company's goal was to actually change its hiring managers' behaviors by increasing the hiring rates of underrepresented groups, notably women and so called nonnational candidates. At this company, nonnational candidates were people who were applying to a job that was based in a different country than their own nationality.

So, for example, a Mexican person applying for a job in France. So we collaborated with this company to create what we call a behaviorally designed diversity training, something very different from the way most trainings in organizations are run today. We created a seven minute video that hiring managers were invited to watch after they had raised a requisition for an open role on their team, but just before they gained access to the submitted applications. And then, of course, we had a control group where managers continued to hire business as usual, no video. And what you're seeing on the screen are the results, the statistically, significant results that I'm highlighting, which show that watching this seven minute video, in fact, made managers more likely to shortlist women, to hire and shortlist nonnational candidates, and especially to hire and shortlist nonnational women, which was the group that was most severely underrepresented both in hiring and in the workforce of the company.

So instead of spending money on a big cons time consuming, expensive training program where they would have trained all employees of the company for hours and hours, the company was able to achieve even better results with a short video that was targeted to the hiring decision that managers made timely because they watched it right before they were able to make the decision, and that was delivered by senior executives at the company that people really knew and respected.

That's another great example of making fairness stick. Small tweak to an existing way of working. And finally, let's talk about making fairness normal. This is all about infusing fairness into the more informal day to day interactions that make up a large fraction of our experiences at a company. At Santander Bank in The UK, men working at the bank a few years ago were asked how many of them were supportive of flexible work. And as you can see on the slide, 99% of men said, yes. I love flexible work. It's a great thing. But then when they were asked, well, how many of your colleagues do you think support flexible work? It was only two thirds of the men. This is a phenomenon, known as pluralistic ignorance, which is where our perceptions of what other people think or believe are often miscalibrated.

And it turns out when we humans are deciding how we ourselves will behave, the perceptions of others or what we think the perceptions of others are are much more influential than what we ourselves believe. And in fact, this is what we found at Santander. When some of the men in an experiment were informed that actually your peers all support flexible work just like you, they were significantly more likely, in fact, 62% more likely to take longer parental leave. This was probably something that they wanted to do all along, but they were worried about social stigma, backlash. They were worried about that long leave taking, having negative consequences for their own career. So they weren't behaving in accordance with their own beliefs, but rather in accordance with what they thought others expected.

So sometimes making fairness normal can be as simple as sharing accurate information about what people are actually doing and what they actually believe to encourage everyone to behave the way they want to. And my final example comes from another ubiquitous feature in organizations, which is one on one meetings between managers and their direct reports. Novartis, the Swiss pharmaceutical company, ran a wonderful experiment on these meetings because their goal was to increase employees' feelings of inclusion and sense of psychological safety. So they tested a variety of guidelines for managers on how to conduct their one on one meetings. And it turns out one of the guidelines was by far the most effective at both increasing, belonging and inclusion and psychological safety, but also increasing the frequency of these meetings. And it was when managers were asked to speak less, listen more in these meetings, and to turn over control to the employee so that their direct report could surface their needs and how they wanted their manager to support them.

Another very simple tweak to an existing way of working that helped the company to create more fairness for everyone. So I hope what's coming across in all of these examples is that making work fair is not a program or a special initiative, but it's a way of doing things, all the things that we're already doing on an everyday basis. I'm very excited to keep the conversation going, so please let's connect on LinkedIn. My name, pardon me, my email is also here on the slide. And if you're looking for more real world examples and a concrete road map for how you can make your own work fair, I highly recommend our book for many, many more ideas. Thank you so much.

Thank you so much, Siri. We shared the link in the chat so everyone will check out the book. And thank you so much for the amazing work you're doing. It's always a pleasure to have you speak at our events.

And looking forward to it. Pleasure.

Thank you so much.

Congratulations on your own book, chief in tech. We're just absolutely delighted.