Speaker 1: Good morning everyone and thank you for inviting us today. I am Lukas Ekonomakis and I'm a structural engineer and really passionate about AI and new technologies. I'm Gerry Maxis and I'm a

Speaker 2: humanitarian architect devoted to social impact work. So this is me and Lukas back in the school days. As you can see we go way back. We actually know each other since we were 10 years old. At that age probably we didn't know what we were going to do in our lives. We probably still don't but what we always like to discover was new frontiers and we like to do that together. At least in our imagination because I think that that boat definitely looks anchored to the marina.

Speaker 1: Well Jay I think what we did know for sure is that it would be great to work together for a common purpose. You see Jay was more about the why and I was more about the how. So we complement each other really nicely. After school we took slightly different paths in life studying and working abroad and then we both returned to Greece. One of the main reasons that we returned to our country is of course the great weather. Well not only that actually we wanted to contribute positively to the many challenges that our country had and still has actually.

Speaker 2: So when I returned in 2015 Greece was deep in the economic crisis and unemployment was soaring. It was clear to us that youth unemployment was definitely something that needed to be addressed. In 2016 I founded Odyssey, a non-profit organization focused on the integration of vulnerable groups through free education and dignified employment. So who do we serve? We serve the most underprivileged youth that are looking to find employment and that might not have the ability to pay for a tuition fee, be that an unemployed Greek or a refugee. What we look most in our people is the zeal. The zeal to radically transform their lives and their circumstances. So in essence these people are our own underdogs. When Lukas joined me in 2018 some of the challenges had already become evident to us in the humanitarian field. Two of them clearly stood out. The first one that really shocked us was that the people in need vastly outnumbered the resources we had available to serve them. For example in Odyssey we are not more than 10 full-time people and we are serving more than a thousand people every year that knock on our doors and that number is growing exponentially. Combine that with the speed a typical NGO raises funds and the problem only intensifies. The second key challenge that we faced

Speaker 1: was how to successfully document and measure the impact of our work. We wanted to communicate our work with our donors and the public and we wanted to be really sure about the quality of our work and what the actual impact it had in our society. For example how effective was a course for helping our people in finding employment. As engineers we always measure and quantify things in our profession so we wanted to apply the same thinking and a similar approach in the humanitarian field. We wanted to make decisions based on indicators and be really transparent about our work. So we looked around we searched for such tools that could help us in this front. However by looking at the main the leading NGOs in Greece and internationally we realized that only a few such organizations have such a data-driven approach. So we had to create these tools ourselves.

Speaker 2: Today we would like to explore how we address these key challenges and offer a fresh perspective about harnessing the power of technology in our work. So I wouldn't say that there was a very definitive eureka moment but through the years in the humanitarian field we actually felt and we quickly understood that we needed data and we needed lots of it. So we completely restructured the way we worked and based it around four key milestones. First we collected large data lakes and learned everything relevant about our people and their needs as well as the labor market needs. We placed procedures and and data insights in the core of our decision making. This resulted into the creation of valuable insights and these insights lay the foundation for the new innovative tools. Finally with these tools we had a chance of scalability and the ability to serve more and more people. So here is let's say a representation of Odyssey. The way I like to see about it is like to think about it as a ship that has many different compartments and rooms. In the center of the ship where the engine lies we have placed our AI tools. So our AI tools gather data from the skills of our people from the labor market needs and they match it to specific job opportunities and specific courses for our people. Our people work as the passengers of the ship and the Odyssey crew works with our people throughout this journey until they're further integrated in the Greek society. Using this approach we saw tremendous improvement in our work. We doubled our efficiency, we were able to quadruple the quality of our services and we were able to improve our monitoring evaluation

Speaker 1: by a factor of 10. So now let's deep dive and look in more detail how we changed our approach to be more data driven. The idea was to bring the best qualities from the technology and the business sector in the impact field and we were really surprised to see these missing. Here you can see a screenshot from our platform that shows in real time data for more than thousand people that we have skill assessed. We document their demographics, their soft skills, hard skills, their educational background as well as their work experience and needs. We have similar data and dashboards for our academy and employability department. We monitor all participants performance and also it's really important for us to take their feedback. But we don't stop there. Even after they graduate, for one year after they graduate actually, we monitor their progress through employment because we want to really understand how well they have integrated. These new insights

Speaker 2: that Lukas described also help us design our new academy center. Specifically we realize that people having these difficulties actually needed an especially truly inspiring environment in order to flourish again. Education does not need to happen only in the classrooms but can be an interactive experience that is both fun and playful. So based on these insights we opted for a more hands-on approach to education. Blending both theory with practice and creating job simulated environments. These courses help them acquire skills not only relevant today but also for the future, thus protecting them from the risk of automation. So bringing our architectural skills to practice we were able to create food in such spaces and these spaces now can serve more than 1,500 people every year. Great. Now let's see something different. Can you

Speaker 1: imagine this? You all know Tetris, right? The game. So what if by playing a simple game such as Tetris, for just a few minutes a computer can understand several important skills you have? How revolutionary could this be for our work when guiding people through employment? This is what we are working on, on a research program funded by the EU called Nadine. By playing a simple game and a machine learning algorithm can quickly assess the skills, hard and soft skills, and can produce such a table with your competencies. Such an approach can open a lot of new possibilities for our beneficiaries, especially those that want to reskill themselves through educational courses. And we have such great examples. For example, Louis. Louis is a migrant from Sierra Leone. Back in his country he had studied law and he was here for a long time trying to find a job and he was really unsuccessful. So his confidence, his bank account were taking a hit and we needed to find Louis a job really quickly, really fast. So we run through our assessment and we discover a hidden talent that Louis has. Although he has a theoretical background, he's really good in technical work and we see that his score in creativity is really high. In parallel, we realized that there is a gap, there is a need in the Greek labor market for metal workers. Actually, Greece brings back metal workers from Bulgaria. So we encouraged Louis to take a metal working training and after a successful few months of reskilling, he found a really quality job in the field. Another example is Fatima from Greece.

Speaker 2: Fatima is a talented chef in Arabic cuisine. However, she has been unemployed for quite a while now, although she has registered with us about, I think, eight months ago. So time had passed and one day we received a phone call from one of our collaborative restaurants looking exactly for someone with a similar profile. So we searched through a database and we found Fatima's profile again and it was a perfect match. She very quickly landed after an interview, really her dream job. So we don't want to limit through until this type of work. We want to bring it to the next level. We want to bring this approach to the humanitarian field and make it as a common standard. So here today, we would like to share with you a new tool that we are developing and something that we are really excited about. It's called the Job Recommender and its technology is based on machine learning that will further help us enhance our scalability and impact. So what does a Job Recommender do? It automatically matches the skills of a person directly with a job opportunity in the labor market and can also work via vice versa. So it can also match a specific person that a company is looking directly to that company. Through this tool, we will be able to provide target job opportunities to thousands of people and thus accelerate impact, reducing our operational costs.

Speaker 1: We are really happy to see technology and innovation pushing the boundaries of the humanitarian field worldwide and actually we have seen great examples emerging on that field and I'd like to share a few with you. For example, in a refugee camp in Jordan, the blockchain technology is being used to support the efficient distribution of goods and services among the refugees. Another great example is Omdina. Omdina is an online platform that brings together data scientists, AI experts and impact organizations to tackle challenges such as wildfire prevention or human rights abuse using AI algorithms and data analysis. Another great example is Hala Systems. Hala Systems is a startup that is using predictive machine learning to protect civilians from bomb attacks in conflict zones.

Speaker 2: In essence, we're in the beginning of our journey. However, we feel that by sharing some of our efforts today, we have offered a glimpse of the tremendous potential for improvement in the humanitarian field. We would love to see youth putting their talents to good use in this sector. Humanitarian work does not need to be obsolete but can rather be a driving force harnessing the power of innovation towards social good.