 So now that we talked about why we might want to have diversity and inclusion in software engineering, let's talk at some of the issues that we're in particular facing right now as a consequence of not having very diverse teams and organizations in the area. And I'll just give two examples, but they just should encourage and motivate why diversity is an important topic that can affect everyone in the end and should be considered. So first off, what we often see is that non-diverse teams build non-diverse products. So for example, consider what I mentioned earlier about disabilities. If you have someone on your team that is for example, red, green blind, you can be sure that they will raise that issue if it's not in your final product. If you don't, then you need to have someone who raises this problem and things very quickly get dropped. Now you could argue that maybe red, green blindness is not as important, maybe, but there are other examples of this. For example, there is a story of a Microsoft and IBM face recognition algorithm that performed super bad on dark-skinned and female faces. And this was noticed after it was shipped, but you would expect if the team would have been more diverse that more developers would have noticed and raised this already during development. So that's obviously a problem because you're shipping a product that doesn't work well and that can hurt the company, okay, that can be a financial issue, but if you then look at what these products could be used for you also get into much more serious issues. So for example, in the case of face recognition, you could very quickly end up in discrimination. If you, for example, try to detect people that are about to commit offenses, criminal issues, there are similar examples of tools that try to predict how quickly prisoners are gonna get criminal again after they return to society. And again, very often these tools are based on historical racist court decisions, for example, and if you have diverse teams, they are more likely to raise these issues, to detect these issues. If you don't, things might get dropped very quickly. So just by having people in your team that are different in certain aspects, you can expect that these differences will also make it into the product. People will raise that these differences might be important and are accommodated for. So that's why you would like to have diverse teams for building diverse products, but then the other challenge that we are facing, the other consequence is that it's actually really hard to get diverse, to attract diverse talent. So hard to be diverse as a company. So there are a lot of companies, there are a lot of universities that try to be very diverse in their hiring, attract women, attract minorities, attract different age groups, but it doesn't quite work. And this is also sometimes known as the so-called leaky pipeline. So it's essentially the problem that you already have, for example, few women in computer science, but then they also drop out along the way. And there are a number of reasons for that that can be tricky to address, and I've just raised them here. There are some good initiatives, there are some good answers to this, but there is no overall strategy that has managed to solve this yet. So just some hints on that. So first of all, role models in any kind of position are known to be extremely good for attracting talents. So this is something that is used a lot nowadays in computer science education that people say we need female role models early on that needs to be clear that it's not just the man doing this job, but of course this applies to other groups as well. So talking about neurodiversity, talking about disabled, talking about different age groups, that can attract talent of diverse backgrounds. So if you see that in a certain job, there's only old people working, then maybe as a young person, you will have a hard time imagining that you could go there. So role models are something that are known to be working very well and known to be very important. Then another point that is taken up increasingly in the computer science in the STEM education world is that you actually need to start early encouraging people with a diverse background. So stereotypes about how, for example, computer science look like can start very, very early in primary school. And that's why there are, for example, in the US, I don't know as much about Iceland here, but I know there are some initiatives, but there are increasingly initiatives to, for example, go into classrooms and teach kids about programming and that it's not just a male-dominated science, which is interesting considering that it started being mainly female dominant. But you need to start early, otherwise it's too late and you have maybe a lack of role models in the beginning and the opinions about what kind of jobs you could go in are essentially gone. Then we have discrimination up here, but similar, maybe not direct discrimination, even though it happens, but non-inclusiveness. So toxic environments at work, for example, you come there as an old person and there are only the young ones that make fun of you and you quickly drop out because you don't feel at home there, you don't feel safe. And that's why the inclusion part is so important. So it's not only about hiring the right people, you also need to keep them and need to make them feel welcome and that's where non-inclusiveness is a big issue and that leads to people increasingly drop out again. And the last but not least, an important topic and there has been quite some research on that is, for example, the interviewing practices. So the traditional interview where there is a person from HR or from any background that sits there and asks you mean questions is known to be extremely biased. So there's lots and lots of research on that for example, candidates that are overweight, candidates that don't fit the existing population like a woman applying for a computer science job at a company where 95% are men, are usually discriminated against. And that can be the important thing is that doesn't have to be conscious by the interviewer but it's something that happens unconsciously that people are sort of pushed away and the interviewer that makes the decision imagines that they don't have the right qualifications or they don't fit in. And there are a number of ways to address this for example, anonymize CVs or have people interview them but they don't make the decision they just have to follow a certain protocol and write up what the answers are but at least try to make this more inclusive and fairer to everybody. Another issue is for example, if we talk about neurodiverse people that interviews can be extremely stressful situations for them and you can make certain accommodations there are for example guidelines for autism spectrum how to make these things a bit more pleasant for them that in a way they don't get an advantage but they get at least to the same level as someone who does not have these issues. So it's about equity, it's about making accommodations it's not favoring anyone over someone else. So these are two examples of why diversity and inclusion matters and why we are not quite there yet. So these things will most likely keep coming up in the news for example but generally in political agendas and organizations so we need to get more diverse to actually build diverse products or inclusive products but at the same time this is a really hard problem so we definitely keep have to work on that. So this concludes the module on human factors too on diversity and inclusion it was just supposed to be an overview but to sensitize for this topic that this is really even though it might not sound like software engineering and computer science this is something that actually matters in practice.