 you for the opportunity. Good morning. I think you had a wonderful morning half session. I caught some parts of it. And here I am to talk to you about Code Without Barriers. Code Without Barriers was born during the pandemic. We saw two trends collide. I'm talking about 2020, early 2020, mid 2020s. One was that technology was overtaking the world. Every organization, every person had to be tech enabled. My mother suddenly learned WhatsApp and Zoom and everything and figured out how to talk to me and her grandchildren and things like that. Every business where it was large enterprise where developers suddenly were remote and had to connect, all the small mom and pop shops which had some legacy system which couldn't take the load of so many online purchases or even the vendor on the street who suddenly had to have a QR code and start doing business through that. So technology just overtook everything. And in the midst of that, there was some technology that was actually changing the way we live and work. AI at that time, the early 20s. And now it's generated AI. It's going to completely change how we live, work, build products, so on and so forth. So when we looked at it, we saw that a few problems arising out of these AI systems. So in 2020, 22 actually, or rather 2020, I think the Dutch court forbid a particular AI-led software which was looking for social security fraud. And the reason it became a big issue came to court and the algorithm failed. Failed in the court, I mean, because they found that what the algorithm was picking up as potential fraud cases were migrant workers, single women and things like that. And that was one case. Another one was in many cities in the US, facial recognition has now been banned from being used in police enforcement and criminal law and order. One of the reasons was one big case which called out that most of, they were using a predictive model behind to see who is a potential risk. And they were using a model in such a case that they were looking at who's a second order of circle, third order of circle and pulling up people who had some offenders already in that circle and pulling them. And in that more, who do you guess were showing up in the US? Who are showing up? The black male, first of all. And so, and then a lot of these models were not explainable. It was like black boxes. So obviously, that was banned. Another tech giant was using AI and recruitment. They use 10 years of data to load their models and then found that women were just being removed from the potential list right off the bat. Anytime there was, because they were looking at who are hired, base is what was on the resume. And so anything related to women, female diversity, all of that right off the bat was getting cut off. So they actually pulled it back and stopped using that algorithm. So what's fundamental among all of the, and there are so many more cases, right? As simple as if you type CEO on the search engine, you will find a truck load of white and now a little brown is added because a lot of Indian male CEOs are coming in. But in any case, it's all male CEOs. Here and there, you will see a few women. So when you talk about potential role models to young girls and women who are trying to get into the field, they go look at, I remember particularly an eight year old girl who looked up, asked her to do software coding. She looked up and said, Oh, it's a boys game. That's all she's seeing is boys playing on that. So all of this combined with the second trend, which was 1.8 X job losses for women in Asia as against men. And this is higher in the West during the pandemic. And interestingly, worldwide, there's 1.1 million gap in women coming back up to the pandemic into the job workforce. And what does that mean for the GDP? That's an entire different conversation altogether. But so we saw these two trends collide and we went to the communities, open source communities out there spoke to them about how can we make them? We first went to women in tech communities. Of course, that is the first stop. And they said, Hey, we're trying to do a lot, but we're not able to make the needle move. And so we went and spoke to the general open source communities. One example is data and engineering Indonesia, the 7000 developers in there, the very active regular feedups spoke to the community leaders. How many women are there in your community? And he's like, what nobody asked me that question ever. So I don't know, but I never see a woman in the meetups. And then the next question, but why are they not there? He's asking back because it said open community, why they're not joining. And so we go back to the women in tech individuals and have a conversation and they talk about male dominated words that are used a speaker, a woman speaker treats questions back differently than a male. Sometimes the men don't understand why. And sometimes the women also don't understand why, which is why we thought that there needed to be an intervention and an intervention at a scale that would make the needle move. And so we created code without barriers. We went back to the drawing table and said, so Microsoft mission is to empower every developer, every person, every organization on the planet to achieve more. How can you do that if you're excluding 50% of the women out there already? So we went back to the drawing board and saw that in our ecosystem, we had a powerful ecosystem and there are a bunch of customers and partners who are looking for talent and we're looking for all kinds of talent and female talent as well. And then there were communities that we work with. So we brought the two together, created this platform called code without barriers. And what it does is it's open to all communities that join. So we have 31 open source communities today, including Fars Asia, Women Who Code is right here, Girls in Tech. So a lot of those communities plus the general open source communities, those are the data engineering AI develops all of those communities and we provide programs to skill and certify the women. That's fundamental. That's a given, right? But more importantly, we went to the customers and partners and said, hey, you need to do something to actually help the women find the opportunities and create diversity in organization. A typical conversation starts with a data science head and you ask them, so you're doing AI. So what about responsible AI? Yes, fairness, inclusion, diversity, all part of the responsible AI. So how many women do you have on the team or how many diverse thought leaders do you have on the team? And that would stall them. So obviously they wanted to, so what they did was about today we have about 52 industry subject matter experts on data, AI, DevOps, you name it, all technology areas. They are ready to mentor the women who have been skilled and certified. So we have a running mentoring circle in the afternoon. There's a skilling panel and Sindhu Chingad, my peer will talk about the mentoring circles a little bit more. But these mentoring circles are crucial because women are sometimes held back by women themselves. And it sometimes starts from home where the mother treats the daughter and the son differently. So we have to get over a lot of that, which we do. The second thing is allyship. So this is not going to happen without all the men in this room participating alongside us, right? Women are only half the equation in diversity and inclusion. The other half is men because men are already there being decision makers, have a voice. Women sometimes don't have the voice and your support is actually going to make that difference if you stand up and voice for inclusion. So mentoring is a big one and our customers and partners are providing the mentors. The second one is hackathon. So you can skill as much as you want, but unless you are doing you're not building the confidence to go and sit at the table and say why you have a thought that's more different and what value you bring to the table. So hackathons have been brilliant because one example in Singapore itself is there was a student from finance and marketing who came into the hackathon, AI hackathon. She skilled AI fundamentals and today she's a Python developer and that was the first interview she went to because post the hackathon she had the confidence to build five, six other projects in a portfolio and then she applied. She got the courage to apply for a Python developer post and she got it and she was like, wow, I'm surprised myself because I'm the first one to get placed from my batch and I got into a tech job. So imagine the potential lying there. So hackathons just build confidence and bring them out. The problem statements come from the customers and partners. So they are real industry use cases like carsome is talking about ranking of cars and things like that. So they bring real industry use cases, support the women to hike on it and then get them ready for the jobs and internships. So the customers and partners bring jobs, internships. We have about 200 data apprenticeships from Petronus, which is one of the oil and gas major in Malaysia, to Prudential, Barclays, Johnson and Johnson. So it's a cross industries. Every industry is looking to build the talent and today these industry, these customers are getting more aware of responsibly building solutions, open source, AI, all of that and hence requiring diversity in it. So my call to you is to come be allies and to the women to come be part of it. You are here. So you're already ahead of the game, but bring more of the women around you participate in all of this and build your story and let there be more speakers and more in AI, especially if you want everybody, like in design, development, testing, user, policy making. So that's the vision and hopefully we will get there. So thank you.