Fantastic. Thank you so much. My name is Christina Ismail and I am the director of primary and secondary education for open ed global. I will be the facilitator for this session. I'm joined by my colleague and friend Marcella, who is also going to be here helping with technology and as our tech facilitator. So if you do have questions or concerns, please feel free to ask those questions in the chat. You can also direct message us through the chat if you would like to. Otherwise we have nothing else to say because we're going to turn it over to our presenter Byron and hear more from him. So take it away Byron. Hi. Hopefully everyone can hear me just yell at me if I, if you can't hear me. Start my presentation. So yeah, I'm joining you from Calgary, Alberta today. It's a little bit darker than I expected this morning, but I was able to find a lamp to use for the presentation so hopefully you can see me. The title of my talk today is called the importance of ethics and data science students and teachers can learn the importance of being human through computational thinking. So what I'm talking about today are some of the major human centered ethical issues facing data science today. I'll give you a few examples and maybe talk about the good side and bad side of those things. Why you should introduce data science in your classroom starting today. Hopefully you'll take that as a takeaway from today and also some data science resources to get you started as well. I'm here representing the Callisto project. The Callisto project is a Canadian government funded can code project and the mandate of the can code program is to provide our students and teachers with the skills to thrive in the digital future of tomorrow. But which is probably actually quite relevant for today with all the online learning going on these days. The organizations behind the Callisto project are actually two of them. So Siberia, which is the company I work for it's a nonprofit it accelerator based in Calgary, Alberta and Edmonton, Alberta, and the Pacific Institute for the mathematical sciences which is a group of Western Canadian math researchers at Western Canadian math researchers who are like dedicated to research and excellence in mathematics. A little bit about myself I guess before I jump into the talk today. I am a project manager and data scientist at Siberia and my data science journey really didn't start by doing anything formal and data science so I'm a, I guess I did grad school as a computational biochemist at the beginning and the computational part kind of came after so I am really like a what have a wet lab background in my history. But then, as I was doing sort of my grad school, I started collecting a lot of data, and I would process it and analyze it and try to get results from it using maybe typical means like a spreadsheet. But I quickly realized that that was becoming unmanageable and I was having too much data to deal with. So I had to learn some techniques to to make my life easier basically. And so I started to learn a little bit of scripting and computer programming. And then as I did more of my grad school studies, I ended up doing, I collected a lot more data and involved sort of multi dimensional techniques as well so there's no way you can actually do that by hand. So I had to figure out a way to do that and process things and visualize things using computer programming and once I joined the professional ranks, I couldn't continue to develop those skills and sort of ended up where I am today. But also realizing that I had to pick up all those skills on my own, and there was no sort of formal education, either in K to 12 or in university that I'd taken or undergrad that I had taken that would have really like that could have prepared me for all the stuff I had to do in grad school and myself pick up myself, sort of on my own whim so I thought, you know, taking those experiences and really wondering what we can do sort of at the four K to 12 folks was sort of what an inspiration behind this cluster project as well. So, love for you to introduce yourselves I know a lot of you already said hi in the chat but feel free to introduce yourself.  Okay, so this is sort of the thesis for my talk today so why are we scared of data science and AI. Now you may or may not be scared of data science and AI or artificial intelligence. But maybe we should consider this statement. So the biggest data companies in the world have grown so quickly that no one has stopped to think about ethics. And in general, there should be awareness about releasing data scientists into industry with weapons that they don't yet know how to use and that's from a UC Berkeley undergrad, a quote taken from a UC Berkeley undergrad. So that's something to think about and I guess the concept of weapons is kind of interesting as well with respect to sort of software. And so, before we get into maybe why we should be scared of data science I also want to share with you why I'm excited about data science and why I really got excited about data science. And so this is sort of some of the inspiration that kind of inspired me to really learn more about his sense and skills in this area. So we did the software which displays it like this. Every bubble here is a country. This country over here is, this is China. This is India. The size of the bubble is the population. On this axis here I put fertility rate because my students what they said when they looked upon the world and I asked them what do you really think about the world? Well, I first discovered that the textbook was tinted mainly and they said the world is still we and them. And we is Western world and them is third world. And what do you mean with Western world? I said well, that's long life in small family and third world is short life in large family. So this is what I could display here. I put fertility rate here, number of children per woman, one, two, three, four, up to about eight children per woman. We have very good data since 1962, 1960 about on the size of families in all countries. The arrow margin is narrow. Here I put life expectancy at birth from 30 years in some countries up to about 70 years. In 1962 there was really a group of countries here that was industrialized countries and they had small families and long lives. And these were the developing countries. They had large families and they had relatively short lives. Now what has happened since 1962? We want to see the change. Are the students right? It's still two types of countries? Or have these developing countries got smaller families and they live here? Or have they got longer lives and live up there? Let's see. We start the world and this is all UN statistic that has been available. Here we go. Can you see there? It's China. They are moving against better health. They are improving there. All the green Latin American countries, they are moving towards smaller families. The yellow ones here are the Arabic countries and they get larger families but they no longer lives but not larger families. The Africans are the green down here. They still remain here. This is India. Indonesia is moving on pretty fast and in the 80s here you have Bangladesh still among the African countries there but not Bangladesh. It's a miracle that happens in the 80s. The imams start to promote family planning and they move up into that corner and in 90s we have the terrible HIV epidemic that takes down the life expectancy of the African countries and all the rest of the world moves up into the corner where we have long lives and small family and we have a completely new world. The late Hans Rosling gave that talk and it I think inspired probably a generation of data scientists. There's a lot of hope behind data science as well especially for things like data driven decision making and data informed policy making, things like that. I think that's sort of the hope behind data science is really that we can use data to do good and to make good decisions and make good policy. So I guess for my question to you folks is I've talked about maybe a fearful statement. I've shown you some inspiration around data science. What does data science in the term make you think of? Feel free to add any comments you want in the chat there. Some context for me when oftentimes we hear about data science in the media, we hear about companies like Amazon and their efforts with their cloud services and prime and anticipatory shipping so being able to maybe predict what you want to buy and maybe ship you something before you even know you need it kind of thing. So that's one way to look at how you can use data science in the world today. Google analytics, everything we do basically on the web, Google analytics is really tracking us in some way and using that information to again maybe show us ads or selling that data to third parties and using that again to try to sell us something. Facebook for example like it started off being something where you can share a lot of information with friends, share how you're feeling, share photos and it turned into a lot of other things as well now. And again associated with Facebook is sort of that Cambridge Analytical Analytica scandal that happened a few years ago. So there's a lot of things that when you think about data science and you hear about it in the media. A lot of these things are tied to big companies. A lot of other things maybe to think about when you think about data science is careers and jobs so when we think about school and students graduating from, you know, high school and undergraduate like the data science field is actually a pretty decent job so this is from glassdoor.com of the 50 best jobs in America for 2020 data scientist is number three. Pretty decent median based salary job satisfaction is pretty high, large number of job openings that's great and if you look closely sort of at this top six list. You'll see a couple other things like data engineer, product manager and DevOps engineer on that list as well and that can also be closely tied to the data science field as well. The data science position was actually the top job in America I think from 2016 or 2015 all the way to 2019 as well so it's been up there and there's still quite a bit of demand for data science jobs in the world so I think there's a lot of, you know, career opportunities as well in this area as well. Maybe before we dive into the ethics side of it. So what is data science. So really data science is quite interdisciplinary by nature. It's a means of gaining insights from data using a mix of math, some math and stats knowledge, computing, computing skills like programming skills. In this Venn diagram we consider that sort of like hacking skills, but it really means like computer programming skills and subject matter knowledge or substantive expertise and data science kind of fits right in the middle there. We talked about a danger zone so if you have great computing skills and you have maybe subject matter expertise in some area, but you don't have those math and stats knowledge. A lot of the results you can get from, you know, applying data science tools and techniques can lead to a stray without sort of those, a sound sort of math and stats grounding as well so we think in order to have a well rounded sort of data scientists you need to be right smack dab in the middle or at least be surrounded by a team with complimentary skills to yourself that could allow you to really thrive as a data scientist. Okay. And so, when I talk about data science really I've described what a data scientist might look like having those mass skills as programming skills and maybe subject matter knowledge. How does data science fit in with artificial intelligence and machine learning all buzzwords we hear about today. So, generally speaking AI is really the big field of study. So it's a blanket term describing our efforts to make computers think like humans more or less within artificial intelligence we have things like machine learning techniques and tools or algorithms. That allows us to improve or learn through exposure to data or experience, depending on what you want to do, and data science isn't necessarily part of the research area but in order to do data science. You want to take and apply those techniques developed by the machine learning researchers and use those with real world data to derive maybe knowledge about business outcomes or policy making things like that. So, that's really where data science kind of fits into the overall picture as well. If we wanted to dive in a little bit more like what are the components of an AI system. So, again, understanding how an AI system generally works and how it can be used with real life examples so the three main components of an AI system and this is just generally speaking are, you know, you have to have data. So you have to have data that you can process and then you need to have some type of learning algorithm usually typically a machine learning algorithm in the middle there. And then the output of the AI system is some type of prediction and diving into these components a little bit more a data set is a collection of curated data and that's really important the curated word is really the important part here. It includes images, measurements, text, video recordings pretty much anything can be turned into a digital format and turned into data these days so video anything you can think of really can be turned into a data set that can be used to train an AI system. And now talking about that middle chunk the machine learning algorithm. So what happens with an algorithm or you know what's the definition of the algorithm. So like, you have some original state. So like maybe the input states, you add some new data. There are some steps in the middle that will change that state and then you have your new state which is sort of the output state so like those are the general sort of steps within that middle learning for machine learning algorithms step. And finally, you have your prediction so like there's various types of predictions you may be trying to predict the weather for the next day the results of the election. You may be trying to predict how groups can be separated into distinct clusters. You might be trying to predict text. So you might be trying to predict your next word in a sentence, you might be trying to mimic how someone writes for example, with natural language or something. So you might be trying to predict what that might look like if they were to write a text about maybe a science fiction topic or something like that. So there's a lot of different things. And so like, you know, all those things sound really great. What could go wrong really with all these things well, there's there's a few things. And we'll talk about a few examples today, where that gets highlighted. The headlines that you might, you might have come across online, for example, looking at media. So online ads for high paying jobs are targeting men more than women, women. When it comes to policing data is not benign. There's books about this topic the ethical algorithm. Obviously Cambridge Analytica which I mentioned before. So all these companies that are applying data science and AI in our world today, they face a lot of conundrums as well associated with the application of, of what they're doing with these AI systems. And one book I really enjoyed reading was called weapons of math destruction by Kathy O'Neill and this is one of the quotes that she has in there basically you can sum it up by saying algorithms are not neutral they encode our biases. In this case, these models are constructed not just from data but the choices we make about which data to pay attention to, and which to leave out. So again, the concept of curation. Those choices are fundamentally moral. If we back away from them and treat mathematical models as neutral and inevitable forces like the weather or the tides we abdicate our responsibilities so she really is talking about not just accepting the results of an AI algorithm but actually questioning and, and making sure that you know we're in a lot of ways. You know pushing back on these AI algorithms a little bit and questioning them or policing them or making sure there are rules in place to keep them from just making decisions without our direct input. So I'm going to show you sort of another short clip here that about a project which I thought was really interesting and talks about some of these issues that we commonly see in the media as related to sort of ethics and AI and data science. I'm Joy and I research how computers detect, recognize and classify people's faces. In my Ted featured talk I spoke about my experience with the coded gaze, my term for algorithmic bias. The system I was using worked well on my lighter skinned friend's face but when it came to detecting my face it didn't do so well until I put on a white mask. After my talk was posted I tested my speaker image profile across different facial analysis demos. Two of the demos didn't detect my face. The other two? Well, they misgendered me. The demos didn't even distinguish between gender identity and biological sex. They just provided two labels, male and female. Now I wanted to see if these results were just because of my unique facial features or if this was something that was more of a pattern across other faces too. So I began a project that became my MIT thesis, Gender Shades. Or the long title, Gender Shades Intersectional Phenotypic and Demographic Evaluation of Face Data Sets and Gender Classifiers. Or just Gender Shades. I wanted to see how well different gender classification systems worked across different people's faces and if the results changed based on somebody's gender or their skin type. I created a data set of over a thousand images of parliament members ranked among the top ten in the world based on their representation of women in power. To get at a range of skin types I chose three African countries and three European countries so I could see how the system performed on lighter skin and darker skin. Then I chose three companies to evaluate, IBM, Microsoft and Face++ which has access to one of the largest data sets of Chinese faces. So now with the data set and the companies I decided to run a test, the companies appear to have relatively high accuracy overall. Microsoft performed best achieving 94% accuracy on the whole data set. All companies performed better on males than females and all companies also performed better on lighter subjects than on darker subjects. When we analyzed the results by four subgroups, we saw that all companies performed worst on darker females. IBM and Microsoft performed best on lighter males and Face++ performed best on darker males compared to the others. Okay, so as you heard in the video, Joy created a new type of data set. So it's a new facial image data set which is highly balanced across skin phenotype and gender. So that's the gender shades data set and as the results of this research. So IBM for example is developing algorithms for detecting, rating and correcting bias and discrimination across modalities both for data and for models. So I think IBM was part of the sort of things that Joy tested her data set against. So it's really interesting that these companies even though they're really large and potentially intimidating, they'd actually respond to something like this where they recognize their algorithms are still kind of a work in progress and they want to be able to correct and do better with that. So that's kind of interesting I think. But really maybe something to think about as well is like how will these better algorithms be used? Yes, facial recognition programs should improve but there are also going to be commercial applications that come out of this that you might question as well. So a number of companies are considering using race classification as a way for businesses and to target buying habits of different gender groups or of different groups. So you may or may not question whether or not that's fair or not. So that's something to think about at least. And so there's more than one example of this sort of happening as well. So we're creating better algorithms or we're trying to create better algorithms to remove some of that bias, but it also opens up other sort of questions as well. Another sort of example I'd like to talk about today is proctorio example, which is around sort of exam proctorings and this has really come up as a result of COVID-19 as well where a lot of universities and are really relying on this exam pro... like this remote exam proctoring software to proctor their exams and it relies on facial identification and tracking. So it runs into the same sort of gender and ethnicity issues that the gender shades example kind of identified as well. So it has some problems recognizing people of different skin tones. It also has some other issues as well. So when you're set up using one of these exams and I haven't actually done this myself so I don't have first hand knowledge, but from what I've read it takes sort of like a widescreen angle of the room you're working in. So there might be some privacy concerns of what's going on. And there's also concerns associated with what it actually does to the individual taking the exam. So in addition to the stress associated with taking the exams, it can also cause additional stress because you don't know what behaviors you're doing may cause a system or the AI system to flag you as acting suspiciously or something. And so maybe you try not to act suspicious while you're writing an exam on top of the additional stress or the regular stress of actually writing the exam itself. So these remote proctoring exam systems are kind of interesting and for one example of maybe some of the concerns associated with it, there's a proctorial University of British Columbia lawsuit going on currently I believe. And in that case, one UBC instructor I think really these are provided links to some of the training videos that proctorial provides and it shows some information about how proctorial actually works. So some of their intellectual property in terms of like how their system works and they didn't want that information to be revealed. So there's there's a lawsuit going on currently with that. So if you want more information about that you can you can grab a copy of the side that can click on those links but again. Sorry. Can you hear me? Sorry. Yeah. I was just. No, it's from me. Okay. So, yeah, there's ethical concerns with sort of like how these exam proctoring systems are being used today. Another example I kind of wanted to talk about as well was sort of these a level exam results from the United Kingdom this past summer. So here's some of the headlines associated with it. You know nearly 40% of a level result positions to be downgraded in England. The UK exam debacle reminds us that algorithms can't fix broken systems. A level downgrades hit pupils from disadvantaged areas of hardest and maybe a contrasting headline don't blame AI for the British A level test scandal. So there's a whole slew of conversations going around what happened with the level results and I'll dive into that a little bit more. So really what what happened here so really COVID-19 impacted UK students who were sort of in their last year going into university. They couldn't take their final exams, but these are I guess high school kids couldn't take their final exams but government really wanted the results for university admissions decisions so you know very critical steps or junctures in these students lives. So in the office of qualifications off call decided to use a machine learning machine learning algorithm to predict a level exam results. And and this is I think where it becomes interesting and standardized grade inflation so there's this concept of certain schools certain teachers, perhaps, you know, increasing grades for students, a little bit above what would be considered normal relative to other teachers in schools in the in the United Kingdom so how could we potentially standardize as well so not only are they trying to predict, you know exam results but they're also trying to standardize them as well. So that's I think where they got into a little bit of trouble. And then the end result of what actually initially happened was approximately 40% of students had their A level exam scores reduced versus what a teacher would have given them so they also had teachers you know what they would would have given the students in terms of grade or predicted what they would with the students have received on the exam and then the algorithm actually reduced approximately those scores for 40% of the students. Okay, so maybe if we we look back at sort of the components of our AI system and plug in sort of a level example, what does that kind of look like. So the main components the main data sets that we're talking about here are schools past academic performance sort of in that first box, the students past academic performance so not just the individual students but the students passed all the students at that grade at the school, their past performance as well sort of how they ranked. And then you also have a third kind of component for teacher grade and rank predictions. So that information that data is then fed into a machine learning machine learning algorithm. And then that would be used to then predict standardized student exam grades. Now it sounds relatively simple but then you realize as well there are some nuances to this. In terms of when they use machine learning algorithm and how they used it really varied so one of the things they did was very depending on class sizes so if you were in a class was less than five people. You know they would completely just use the teacher kind of grade and rank predictions. If you were in a class size of five to 15 people. They would actually use a mix. So they use a mix of the machine learning predicted exam grade, and also the teacher predicted exam grade. And then if you're in a class size of 15 and above, which is something you typically see in public schools, it would just use the machine learning predicted exam grade. So that's, that's kind of where they got into a bit of trouble as well. So class sizes had a big impact on the predicted standard student exam grades. So if you wanted to take a deeper dive into this, I provided some links in here but this is an example of how students typically achieve that the grade students typically achieve under a level exams at a school. So this is one example of an input they would use for the exam for the exam prediction scores. So yeah, I provided a couple links that kind of dive into this a bit deeper. But basically, and I think a lot of it has to do with the class size issue. So if you were in a class with five to 15 people, typically speaking that would more or less come from a private school or those in those who lived in areas with the higher socio economic sort of grounding so students and lower socio economic backgrounds, with the higher classes were more likely to have their grades decreased, because they're using just purely the algorithm in this case to predict their exam grades, and they're also because they're trying to standardize those exam grades. While those in what their areas were less likely to be increased, because, or less likely to be decreased sorry, because they were mixed using a mix of the predicted and also the teacher grade so I think in some cases they would actually end up using the teacher grade. If it was higher than the predicted grade kind of thing as well a higher proportion of students from independent or private schools receive top grade so a star and a increases compared with those that comprehensive public schools. And so one of the main takeaways was that the inclusion of past school performance and class size data benefited students from private schools so you know it wasn't that it wasn't necessarily that they didn't have intentions in doing what they did. But the fact that they started to introduce additional factors. And depending on the data sets they use as well they encoded biases that tended to favor or benefited students from private schools. So I think after math, it worked as intended but seemed unfairly published unfairly punished students of lower socioeconomic status. There was quite a few protests but the fairness of the results so students definitely pushed back on these results. And you can see examples in social media where you can see students who wanted to get into medical school for example they were, or thought they were going to get into medical school ended up not getting into it because some of the results. And the level results. They basically decided that have been changed the higher for everyone changed the higher the teachers or the algorithms prediction so try to be more fair in the end that's what they ended up doing. And also another sort of side note but also related was that in the European Union they have the data protection policy framework the GDPR, and that actually places strict restrictions on organization. It's making solely automated decisions that have an effect on individuals. So that the GDPR law requires that processing to be fair even when decisions are not automated so it's interesting to see that, you know, they already have a framework in place to help, you know, keep these decisions from being completely automated. And so there are some protections in place for individuals to protect your protect themselves against algorithms and in combination with, you know, public outcry you can actually make quite a difference that you don't necessarily have to accept the results, like sort of what Kathy and Neil was saying you need to kind of push back and sort of push back against the results of these algorithms. Have I heard of instances. There's a question here about hearing instances like this happening in other countries, not specifically with respect to exam grades. I would say there are some examples like if you've read Kathy and Neil's book with respect to like K to 12 teachers for example, they do use sort of like a ranking systems on teachers so there's evaluation systems on teachers that have sort of an AI algorithm or machine system behind the scenes that help, you know, decide on whether or not teachers should be hired or like, you know, given sort of advancements and stuff like that so there are examples of that but I don't know about students directly being impacted by a algorithms. And I guess, generally speaking, you know, what's causing the majority of these AI problems that we hear about so a lot of it has to do with the data itself so there's, like I was saying there's three main components. There's the data, the machine learning algorithm and the prediction, the data makes up a huge component of this so like the machine learning algorithm is in itself neutral. But once you feed it data that is biased, it's going to pick up on those biases and you can't, or it's very hard to determine what biases are existing in your training data sets without actually going through the process of trying to, you know, predict something and seeing what the results are so it requires a lot of work to actually figure out that there are biases in your training data sets as well. In the most cases for bias, we're talking about data being unrepresentative or and or it reflects existing prejudices that, you know, we may not even be aware like we may not consciously think of but are actually encoded in in the images or the texts that are included as part of the data that's being fed into these algorithms, and the resulting predictions. There's a lot of work being done to look at those, the data sets itself, and definitely like that that gender shades example is a great example of this. But there's also an interesting example where one of the first, I guess, first data sets used for image classification like we think of image classification it all started with like, you know identifying whether it's a dog or not type of thing or a cat or cat or dog or something like that. And so one of the the first image training sets that everyone's who's done data science or machine learning has probably tried out at some point. It's called image net. And that recently was found to be quite biased, even though everyone to everyone has come across this data set at some point. And so it's really interesting to kind of go back and realize that even though we've been aware of these problems. We've still used some of these data sets without even really understanding that there's a problem with it.  So in the case of the A level exams, you know, predicting students grades is probably quite a fair thing to do with the with the AI algorithm or could be argued to be but also standardizing the student grades across schools across teachers, things like that like that's, that's where you get into a little bit of trouble I think when you start to combine sort of your, your what you want out of the algorithm so really you have to frame the problem with a very sharp lens I would say. And so the decisions are made for often made for various business reasons or other other than fairness or discrimination so we really when you ask questions for the of the AI. So you have to be really sharp questions and other problems, just generally speaking transparency and how the AI is applied so for like how it's applied for example like you don't necessarily want a completely automated decision coming from a you want to have it sort of augment a human to provide sort of like an AI, you know, driven or you know instead of a data driven decision maybe it's like an AI driven decision or something, but it's still the human in the end that has to decide whether or not there's a problem, and whether or not to use the AI to make a decision so I think it's important to have like the human in the loop concept to be at the front of the mind and actually like use the AI to help augment sort of the humans decisions. Okay, I guess, one thing we should talk about as well as the potential of data science and artificial intelligence so AI algorithms and their applications are really ubiquitous these days and have the potential to improve lives so like they're just they're really everywhere. They interact with technology. I think you, there's also the concept of actually being able to use this in the classroom yourself as well. So locally so like in your classroom for example grading and assessments can likely be made quite a bit more efficient through automation and use of algorithms. One example we like to talk about is we've run student hackathon so at the Clista project we've run student hackathons before, and it's relatively straightforward to review like a handful of submissions. But for example if you had something where you had to review hundreds of submissions, you would probably like some way to automate that and make it a little bit easier than having to to go through each and every one because it would take a long time. It would just take a lot of effort for you to do that so if you're an individual teacher for example, it might be like, it might be impossible for you to get through all these things so there's our ways to make your lives a little bit easier with AI and data science. Another example from the curriculum in Alberta is career and life management. In that sort of part of the Alberta curriculum, one of the things they ask you to do is ask the students to do is actually write a resume, and the teacher is supposed to look at that resume and provide feedback on that resume. Even providing feedback on it for a class of 30 on their resumes, you can imagine being quite difficult. Now if you imagine a class of 100 or 200 or 300, like it becomes quite overwhelming. And so it's quite difficult and there's probably some consistencies that you can automate and provide feedback on. And I guess another aspect of that as well is when you actually get out into the workforce these days when you're submitting your resumes as well. You're oftentimes going to be submitting to a system that a company uses that probably has AI incorporated in it, which will go through a checklist of things that it's looking for and if you're not if you don't necessarily have that thing in your resume that it's looking for or you wrote it in a funny way. So that the AI doesn't recognize you might get filtered out as a potential candidate for a job without even realizing it, even though you're, you think you're fully qualified for it so. There's some concept of being familiar with interacting with the AI systems as well that we want to be able to teach our students. And as well I think what's also interesting about data science and artificial intelligence is the open source technology behind a lot of the AI and data, data science tools are actually available to the public and we're actually made available to the public through research through just individual and group efforts to make these tools available to people so a lot of those tools that companies are using or actually based off of open source or public technology and they've, you know they've taken that in house like in these big companies and turn it into their, to their intellectual property so it's kind of interesting that there's a lot of, I guess origins roots of this in sort of the public sector as well through open source technologies. So, you know what can we do so how can we sort of improve our trust in these AI and data science algorithm so what concrete steps can we, we take to improve our trust in the algorithms that run our lives. So really I think, taking part and getting involved and getting your hands dirty with with these, these tools and techniques. So you can talk to data scientists AI researchers. You can try out some of the groups you can try out some of the tools and techniques like there's lots of tools and techniques out there that you can just try and they're open available to the public. They're freely available. It's pretty amazing actually. There's different social meetup groups around data science and AI so like in Calgary there's, you know, quite a few of these groups and similarly throughout the world there's different cities have large social meetup groups and it's just a nice place to have a conversation about data. You can also directly participate in the data hackathon. These social meetup groups will oftentimes organize data hackathons and you don't necessarily have to have technical skills to participate. You just need to be, you know, keen on participating and getting involved. So it's kind of interesting that, you know, you can get more comfortable with these technologies and concepts by participating in it. So we have run student hackathons for example we have other groups that we've worked with like STEM fellowship and let's make it count out of the US. So there's a lot of like opportunities to get involved and actually participate. What you can do directly in the classroom if you're a teacher, you can introduce computational thinking and data science concepts and skills. The Cluster program has a bunch of resources so I've listed some out here. So we have like getting started materials, curriculum modules, lesson plans and we have an online, we have a couple online courses as well that are free for teachers to use and we kind of target grades five to 12 teachers. So feel free to use those or share those with any of your colleagues. You can also do different activities that don't necessarily require sort of a deep dive into the technical side of things. So there's we have a data visualization of the week activity. There's also different curriculum made, for example by MIT, the AI FX education curriculum, which I really think is fantastic and I've provided a link there as well. I want to background about some of the tools that we use at the Cluster project so we use the Jupiter ecosystem so a Jupiter notebook is really what we use to showcase some of these data science techniques and concepts. It's an online document that includes both text and live Python code that you can run. And these documents, you know, they run on our service that we've made available for students and teachers. But you can also run them on commercial services as well, such as Google and IBM and all those things. So yeah, we have all that stuff available through our Cluster.ca website. Yeah, and just maybe another comment about what the data science community is doing. So the data science community is it recognizes a lot of this problems and ethics associated with, you know, AI data science as well. So the Association for Computing Machinery, the ACM, they have a quote saying that five years from now all students majoring in computing will have an experience using computing to better the world. So this almost like data for good concept or AI for good concept. UC Berkeley now has this human context and ethics curriculum as well. So if you're an undergrad there, you can, you can, you can get involved with that. And for algorithms themselves, there's been a big huge push around interpretability. So oftentimes we can, we look at these algorithms as kind of like black boxes, where we throw some data in there, we get a prediction. But like I was always, as I was saying, to understand what biases are actually built into the algorithms, we need to be able to really interpret and understand how these algorithms are working. So there's been a huge effort by big companies like Facebook and Microsoft, Facebook, Microsoft and Google and all those big companies around interpreting machine learning. So interpret ML, fair learn, open differential privacy. So these are all sort of machine learning algorithms that are actually quite interpretable and have tools built in to allow you to interpret what's going on. So that's really exciting. There's also algorithms and concepts like human in the loop where before you make your prediction or before prediction is implemented, you want to have a human or a set of humans there to really verify what's going on. You don't want everything to be sort of automated. There's also the concept of auditing these algorithms. So before you release something to while you want to have a third party come in and audit them to make sure that what's going on is fair and again constant and again like monitoring of what these algorithms are doing is really, really important. There's also a project called open DS for all and it has a lot of interesting resources for people to take a look at and it's, it's creative commons license as well I believe so you can reuse and remix those contents as well. Yeah, I just wanted to wrap up I think I'm almost at the end of my time now so I have a feedback form feel free to fill that out. I have a link here I'll just slide it into the chat. Feel free to let me know how I did today. If you have any information about the project I'm involved in close so you can email us at contact at closer.ca or you can check out our website, and that's just some of the information on how to contact us. So, I think I'll stop there and if you have any questions feel free to let me know. Thank you so much for this. We will open this up to any questions that folks may have in the room. Feel free to drop them in the chat or turn your microphone on and ask him directly. This is all incredibly fascinating. I was a part of the public interest technology team at New America, which is a civic and think tank civic enterprise and think tank in the US. I started with the idea of getting people interested in technology but not to be working at Microsoft and Google, but to be to be public servants, and to work in government and and bring their technical skill set in and so it's a very new field and they're a second year as far as the university network where they're providing grants to universities here in the US to actually build programs. And that involves the curriculum so it has to be multidisciplinary like you were showing that diagram event diagram of how it is very multidisciplinary and bringing the different colleges together can be incredibly challenging and so it's just really interesting to drop the link to this year's grantees. There's a YouTube video that kind of introduces the folks that'll be doing things this year. It includes a lot of your Ivy League schools here in the US but it also includes like Miami date college, because they have a large, you know population and they want to get folks interested in this so this is all really, really interesting and very helpful so thank you so much. Hi, you're welcome. Yeah, there's it is a pretty new and even like the concept of data science and K to 12 is pretty new. So, yeah, there's a lot of ways, but what's really cool is like, like I was saying a lot of this comes from like the open source side of the technology fields and all of it is actually available for you to poke around with and play around with for free with your computer so like it's really interesting how it's kind of evolved and how big companies are taking this but now they're starting to recognize that they need to also understand what's going on with these algorithms and also give back to the community as well so they're making a lot of effort to a lot of efforts in these areas as well. Byron, would you mind dropping the link to your slides in one more time just because we had folks join after and they won't have access to that. Oh yeah, for sure. Okay. Thank you so much. Yeah, you're welcome. Are there any other questions or comments for Byron. All right, everyone give Byron a round of applause. Thank you so much. Thank you. We will start to transition to our next presenter now, Connie, I believe you're


Welcome to Beyond Code, the importance of elevating non-code contributions. My name is Fatima. I'm Abu Bakr and we are from GitLab. We're on the developer evangelism team that sits under the developer relations team, where we work on DevSec, AI, ops, and building community. So today we wanted to talk a little bit about non-code contributions, what examples of those might look like. We'll share a little bit background on our experiences working in open source communities and at GitLab. And then at the end we want to have this open forum discussion. So if you have reflections as we're talking or if you have questions, please write them down. And then we'll have 15 minutes or more at the end. We want this to be like a very open format. We want to learn from you as much as we tell you what we've experienced. Okay, we'll start with me, my story in the community. My career has involved community, right from all the way back to 2004-2005, when I was still in the high institution at Federal Technical Biology. Here are some pictures of some of the sessions that I had then. The main goal of the community then was to... I wanted to have an environment whereby I can learn from my friends, hang out with my friends definitely, and also share knowledge. It evolved from just being a group of friends, learning with one another, to members of the institution joining and coming together, and also members from the city, even sometimes members from out of town, joining to come and be a part of the community. And the main goal is to learn, share, and grow with one another. Even when we started partnering with organizations like the Google Developer Groups, Digital Ocean, and a couple of organizations like they do today, they defined their KPIs and other things for engagement, the goal still remains the same, to learn, share, and grow as a community. Fatima? So my background in open source came because of a job. I had gotten a job that they wanted me to learn Drupal, and they said, you need to go to this conference, you need to figure out what this project is, get involved, figure it out. So I went to DrupalCon, and in that community they say, come for the code, stay for the community, and it really rings true because you go there in order to better understand the project or contribute a component or module, but you end up meeting all of these contributors from all over the world and feel really connected and like part of something bigger. And so over the years I got involved with community contribution, I became a core contribution mentor. The first photo there is like the mentoring group in DrupalCon Vienna, which was really cool. It was my first time like walking somebody through the process, like a new contributor walking them through the process of contributing something, and when you see someone's face light up because they've made a contribution, it's very rewarding for yourself as a mentor as well. So like GitLab, Drupal is also a very complex project, so it's not always easy for people to just show up and contribute. You need to have a lot of background, you might need to have specific technical skills, you might need to know a specific language, and so there are other types of contributions that are also very valuable, like writing documentation or testing or writing an onboarding guide or helping somebody translate something for another community. And so those are the kinds of things that we want to share. We're going to talk about a few of the different non-code contributions that we've worked with, and then some examples of programs or initiatives that you can do to support those types of contributions. We're going to start with events. Yeah, we are here, Open Source Summit, an event. So events are a huge part of non-code contribution. It brings the community together to come and learn from one another, share experiences, share learnings, and even create more opportunities for the future. Well, all these events involve a lot. For example, these events took months of planning, months of logistics, working with partners, ensuring security. You are going to be dealing with humans, and humans are complex. Ensuring safety and coordination, inclusion of everyone is a huge task that every community member has to do, not to talk of venue management. And this is at a large scale. Now, if you come down to Meetups, some of these organizations already have partners to work with. Oh, they can tap Google, Google, give money, and so on. But you in your local community, you have to go convince some sponsor or some organization and show them this is the value of this event. This is why you should support this event and so on. And this event at the end of the day brings opportunities for the community to grow. That is where new contributors are initiated. That is where new members have ideas on how to contribute or how to influence other parts of the community. And even non-code contributions, that is where people have ideas on how, oh, this is how we can grow the community. This is how we can make it inclusive. And this is how as a community, through events, we can grow better. Fatima. So with Open Source Projects, there's a number of different things that go into that. We've listed out a couple here, like documentation, planning your roadmaps, your community, your customers, your users. They all want to know what you're building next. So if you're organizing an Open Source Project, likely you are also planning a roadmap, planning future features and releases. You also want to onboard new contributors. You want to mentor them, have training, so that you can continue to retain them as they have experienced contributions to your project. Then there's the hands-on programming part of it, like triage and feature discussions. And what we find, even with contributors to GitLab, is there's one person who writes the patch. But there are so many people that go into the process of getting that patch committed to the product. There's people who provide suggestions. There's somebody who jumps in and says, hey, I tested this, but it's not working. Here's the screenshots. Here's why it's not working. And so all of those people don't tend to get contribution. It's usually the person who committed the line of code that fixes the thing or adds a new feature. And so I think it's important to take a look at that whole process from onboarding that new contributor to mentoring them, to them filling out an issue or reporting a bug or helping somebody review an issue that they've created for. And so there's like a number of valuable contributions along that kind of life cycle of a contributor that I feel that we don't recognize yet and that I would like us to start recognizing in the scope of open source projects. Yeah, next thing is community management. Yeah, how do you keep all these communities together? Okay, you organize events. They attend. So people come to learn. Some people come for swag. I for one, that's part of one of the reasons why I come to events. Then others contribute, but at the end of the day, how do you keep everything together? How do you bring in new contributors, maintain them, enable them to grow through the communities? So you don't keep dealing with one of the issues I had while I was managing events back home in Nigeria is every day people, you are dealing with newcomers, new people, you have to maintain new content. How do you get those newcomers through the stages to become contributors, to become future speakers, to become non-code contributors in the future? So creating those pipelines, defining a goal as a community. Why are you having the community? What is your end goal at the end of the day? And what are the KPIs? Are you just worried about numbers? Are you worried about monetary aspect? Are you influenced by an organization that has objectives to find? All those have to be in place. Remember retention. How do you want to keep your members?  Or they feel this is too basic for my experience. Or the community has played to, there is no much growth that members of the community can achieve. Then content management. As a community we generate a ton of content. How do you coordinate the articles, the videos? This session is currently being recorded. How does it become valuable for members of the community? How do they find it? How do they benefit from it? And also code of conduct. We are complex human beings. And from modes to cultures. What is right? They call it moral relativism. What is right? One side might be wrong in another side. What is offensive is on one side. They are generally general things that are offensive. But then cultures differ. And how do we ensure that as a community we all become inclusive. We all coordinate and work with one another in a safe and secure environment? Yeah, Fatima. So now that we've covered some types of non-code contributions we have some ideas of how to recognize them. And then we're really looking to hear some of your ideas on how to recognize them. So one of the ways to celebrate your community members is giving them achievement. So you could do this through badges. We're implementing a badging system on GitLab.  But there's a lot of platforms that allow you to do that as well. There's like Creadly where you can set up badges and share them with community members. If you're already on certain community platforms like Discourse or Reddit or Stack Overflow they tend to have their own systems for gamification and awarding contributors or recognizing them. A lot of companies like ourselves we have a GitLab Heroes Program. Advocates is a great place to give recognition and see what they're working on. Some of our heroes are very technical. Some of them post for us with code snippets and all of that. Some of our heroes are not so technical but they can provide feedback on initiatives. They can test new features that are in experimental stages. They can come to events and talk about their experiences and inspire other people. And so really figuring out who your advocates are and how you can reward the different types of work that they are good at. And finally like people like Avamagra who loves swag you can always give swag in exchange for participation. But I think something more powerful than sending somebody a mug or a backpack or a pen like these things are great is actually giving them opportunities. So maybe you have a learning internship at your company. Maybe your project does a shadow program. We have those at GitLab as well where we have shadow programs. You can shadow the CEO. We have a development director program where external contributors can actually shadow one of our development directors and see what the day-to-day of working at GitLab is. And so I think some of those opportunities are actually very valuable. So if someone is contributing to your project this might be a way to give them value back. And so it's a good relationship. And then finally. Yeah. As contributors we all hear it online. A very good and hard-working contributor suddenly says he's taking a break. Or for some reason he's going out of the community. Not because of anything but because we are all humans and life happens. Sometimes it's mental health. Sometimes it's just burnout. People have been working years and years and we all went through the pandemic. Everyone had their experiences from the pandemic and wanting other communities. How do we ensure members of our community are held in the stay safe and an environment is created for them to feel safe and also be able to share and learn from their experiences. Not just the code part but how some of us are parents. Some of us are passing through some challenges in life. Some of us are caregivers. Some of us are in environments that have complex situations, political situations. So being able to create an environment where we share, we learn before comfortable and where possible we feel vulnerable to it because sometimes it's not about fixing it. It's just about being able to let out, share with someone and learn from someone. And as a multicultural community almost all of our communities are multicultural. Learning from a different culture, a different environment. Create a system whereby we're able to become more inclusive. Be able to become more welcome into others because you only be able to understand and welcome someone let's say from South Asia or Africa or understand their situation if you know what they are going through. If they've been able to be vulnerable with you and be able to share. You even know where to support better when they are comfortable sharing with you or sharing their experiences of how they are, whatever they go through. So creating a healthy, safe environment where we can be vulnerable as a community is very crucial in how we maintain communities. That's why communities need to have groups or communities that ensure safety, mental health and a more conducive environment for contributors within the community. So we've covered the importance of non-code conversations some examples that we've seen from event planning to parts of the open source project cycle to community management as well as having non-code conversations as well as contributions. So we wanted to have an open forum discussion with all of you today. We've prepared a slido with two very general questions so that you could submit questions if you feel uncomfortable speaking on the mic or on the recording. So you can submit questions to the slido using this URL slido.com and then the hashtag number which is one nine seven nine six four five. And then we'd also love for this to be interactive. So you can submit questions there but you can also submit reflections, things you're thinking about. And if you feel comfortable we have a audience mic as well so you can come up and share a reflection or question with us. Share something that maybe your company is doing to recognize non-code contributions that would be great. Abu Bakr is going to get the mic. Thank you. Two participants are typing. So Abu Bakr what's the non-code contribution that you've made recently? I spoke yesterday. I spoke about understanding the sake in DevSecOps. Security is the major part of the major issue in our industry. So I spoke about people who are not familiar with security. How do they apply? What are the different attack vectors? How do they mitigate them? And how do they automate their software development lifecycle to be able to do that? Oh we have a couple. Oh podcast. Almost everyone who's seen podcasts these days. Yeah, I know they're great. Yeah. Because you can listen to a podcast while you're walking somewhere and it's a great way to feel connected to somebody that you may not get to see in person. Yeah. And we are getting to learn more about people from different parts of the world just by listening to them on podcasts. Yeah. Develop training. Yeah. That's interesting. At least so that others can be able to learn more. Yeah, it's always great when so if we have a community member who has put together a training, they'll share it with us or they'll share it on their social media and then I put together our community newsletter so I don't actually write most of the content. You know, like I just start collecting all the content. So if I see someone's done a podcast, I will just grab that and put that in the newsletter. So that's my non-code contribution. There's no code involved. It's a drag and drop newsletter editing process. Attending this session. That's a great one. Yeah, it is. Because at least you've come here, you've learned something. You be able to, even if it is you attend another meeting somewhere and share some of the things you've learned somewhere. You've contributed to a project. You've contributed to Open Source. Does anyone want to? Yeah. At least I get to exercise. Great. Thank you so much. And I'm Petia from Humanitarian Open Street Map team and that's a big passion of mine on non-contributions. I'm not a software developer myself but I focus on tech engagement. And yeah, a few examples. I think we had an outreach mentee for instance and our project was exactly about technical documentation. And I'm really big advocate. I always say like other examples that we do is I run a working group where people can join. Maybe one thing I don't see here is also testing. So involving a lot of our users in testing the software before we deploy like on staging. And there's been really interesting feedback because normally people say I'm not technical. But actually there are contributions. So yeah, just wanted to say thank you for having it really important and great to hear all the examples. Thank you. User testing is a really important one. Like I worked out the city of Boston and you don't know if people are able to load the website or use it or if it's accessible until you put it through that test. Thank you. Hey, so I lead communities at the Green Software Foundation and we are about half of our work is technical and the other half is really building communities. So I think I put Meetup Group on here. Podcasts. But a lot of our work isn't just building the tools for people to have greener software. It's also helping them learn about it, helping discover in the first place, spreading that awareness and building standards. So actually while code is a big thing, it's probably only 50% of what we do. Wow. That's really great. Yeah, it's awesome. I think the Linux Foundation houses almost everything around Linux, cloud native and everything. And the platform, for example, I am aware of the LFX platform. Yeah, so almost in the infamous or famous CNCM landscape with all the logos of projects and everything. So it's very good to have a platform. It's part of enablement. We're able to enable the project to grow because at the end of the day when project people have contributed, suddenly you see a project dying or you see the project ripple and it's only been updated nine years ago. So it's a very great work that foundations like the Linux Foundation are doing to keep our projects healthy. Thank you. One story I yesterday was one of the speakers that attended this session. He was able to attend the open source summit because he attended an event back home in the open source community Africa. And Chris was there and he gave a voucher that, oh, if you want to go to the open source summit, you can come. And he was here yesterday speaking about observability. So it's one of the success stories you hear from this event, this community. You don't know where opportunities can take people and how they benefit the community further. Okay, awesome. Open tofu. I just read it this morning. Open TF became open tofu, right? Yeah. I was reading about it this morning. Awesome. She accepted our open tofu. Nice. Okay. Next question. This may or may not be a controversial question, but when you're talking about non-code contributions and a lot of projects they measure contributions. So they might give you points for specific things that you do. If you're a contributor on stock overflow, for example, answering a question is a certain number of points. Upvoting a question is a different number of points. If you get the solution, you might get plus 30 points. There are different ways to recognize and weigh non-code contributions. This is something we're still figuring out as well. So I thought this would be a good open discussion. How do you recognize them? How do you weigh them? Are you rewarding them? Are you sharing them? Yep. We'd love to hear from you. Or you can take the mic. We'd love if you would take the mic. Badging. Awesome. Yeah, badging. Yeah, that's a nice one. Director of Champions and Experts. I am a part of the Google Developer Experts. I think there's distant JetBrains that can give you one-year license to their software if you are listed in the directory of the GDEs publicly available online. So I think that's why directory is important. And it's also a recognition for you to say, okay, to recruiters or employers. It's recognizable. It's not just by a word. Yeah, and that brings you to other opportunities as well. If we invite our heroes to speak at InVent, it's very good for them as well as it makes us look good. But it also makes the hero have the opportunity to network at the event to share their expertise. And then demonstrate the impact of the contribution. I love that because sometimes, like you were saying, people will say, I'm not technical, but they'll write a really great explanation or documentation for something that someone who may be technical but may have never touched this part of the code or learned about this function will be able to read that and understand. And so sometimes I think we make contributions and they kind of go into this void and you never get to see the impact that your contributions are having. Yeah, swag. Yeah, definitely swag. I'm tired of swag. I have a funny story. When I was hosting the digitalization community back home in Andrea Balchi, so the first time they wanted to send us swags. So we were just expecting probably a few hoodies or socks. But they ended up sending like cartons of different swags, t-shirts, different sizes. So at a point we didn't know what to do, so we organized a very huge event to do and so on. To the extent, anywhere in the whole town if I see a digitalization t-shirt, I know it's from that event. That's amazing. Like you're all connected by this giant box of swag. And contributor contributions. I think one mechanism by which we can do that is through our newsletter. But the newsletter is also limited because the community that's reading it is also the community that we're sharing back to. And so I personally would love to hear other examples of ways that we can share that impact to... The people I want to reach are not the people who are already reading the newsletter. I want to reach the people who think that they don't belong in that highlight, contributor highlight section in the newsletter. I'm nervous about it or like, oh, I don't have anything to contribute. Where can I meet those people and how can I encourage them and know that their contributions are valued? I don't know. That's why this is a bop. Maybe you can put it on social media, so like just like one person a week or someone who has contributed a lot recently. You can even do it monthly. It depends on how many contributors you have. So maybe something like that in a social media post which will also be good for the newsletter but also recognize who is contributing. So maybe something like that. Have you done that before? No pressure. Yeah, so we did consider it. I was a community manager at another organization. So yes, we have... So whenever someone got any kind of a title even if it's like becoming a community manager or recently becoming what was the name? Like a contributor but someone who has contributed frequently a member maybe. So yeah, we used to do that. So yeah, like tagging them giving them that recognition because at the end of the day the organization will have a lot of followers. So that usually matters and it makes them feel good. Awesome, thank you. Thank you. I was also reading this about the impact of contribution. I just share and use a few. I think that is a very great point because if people are able to see that oh, this company or this organization actually took our feedback and change was made or an impact was achieved it was for more people to share feedback and be able to... I think that's a very great point. Anyone else? Yeah, I think... a question I've always wanted to have I don't do live streaming here. Oh, okay. I've always wanted to know how I'm always fascinated about doing live streams but I end up not doing it. So you see how it actually impacts not just good contributors but non-codes to be able to contribute back to the community. Because most of the videos or live streams you see online are just someone live coding or creating some examples somewhere. Any PJ Mads who used to live stream quite a bit and so this is something that he related to me is that it really lowers the barrier to feeling technical because your live streaming while he did a lot of Python bots so he would be... I'm trying to figure out how to do this but I don't even know how to start this function and part of this live stream is him just googling it and figuring it out and going and we're sitting in the chat and we're like no PJ that's not the right function but I think people in the chat being like well thanks for doing this because now I don't feel like I'm really lost and so it gives people a moment to feel like relatability where it's like oh you're struggling too I struggle the same way or I always forget where the documentation is as well and so from what I've heard I think live streaming helps lower that barrier but I think it also depends on what the project is and who's watching if anyone. Also mentoring how do you think mentoring because I see mentoring as one of the things that actually spurs a lot of comments because a lot of people feel uncomfortable don't know where to start and mentoring seems to be one way to be able to boost more of non-good contribution. How have you seen the impact of mentoring? In my experience I have mentored more contributions than I have done contributions so like if you track open source contributions it's always like a comment where sugar overflow helped me so I get no credit for that you know like the credit goes to the person who wrote the patch or they reviewed a translation or they uploaded like some testing information but I'm sitting there like hmm if I were you I would you know and so it was a really good experience for me because I used to feel not technical enough and my way of like overcompensating for that was I'll just mentor and then that person will do the hard stuff and I will help guide them but I learned that like it's very hard to be the mentor because you kind of have to like sit on your hands and you want the person to learn the process so that when you're not there they can do it as well so it was a very good learning opportunity for me to like listen and like observe and like not say anything until they're ready to receive like the next step or a hint or something so yeah I definitely learned a lot. Yeah awesome and I think it's also valuable to managing a community because definitely some older folks from the community would live ok life will happen people will move on to some other things so being able to when mentees will then grow to be the new managers of the community that will grow to the community in the early days of communities we all know oh Kelsey Hightower Brian Lowell, Joe Bedan and so on but now you hardly even hear about them in the community dude they are still a part of the past when new people are now maintaining the community and growing the community as as we go. And it's important to have experts in different areas if I'm good at back end engineering I'm probably not going to be good at design right and so you need to have people in your community that can be able to share their area of expertise and make space for sharing that. Yeah awesome. Anyone wants to share anything? Or we can stop the session and have a little conversation without the recording. Maybe first an answer that's somehow not really there in my opinion but it's really easy just the next time you see the guy on the hallway in the canteens I'm coming for corporate just tell him in person thank you so much and it's really an in person thank you is so much worth you see the smile in the face he sees your smile in the face it's really nice. Yeah I definitely agree because I think there was a time I met someone somewhere and he was kind of sharing how he got into tech by attending one of my sessions and I was in at very low states at a particular period in time but the person sharing our experience about something I did I didn't even know impacted anyone and he was not how that's for him to take and how he's able to achieve this get his job in this place and so on it really boosted me and gave me that morale to Bokeh. I think this is working and doing more will definitely benefit more members of the community. And second thing I have a general question about non-code contributions maybe you got a good answer so if you go to a project it's always you go to REPL and you see issues. About non-code contributions is a little harder because the work is usually going on in the background deep inside I don't know some forum maybe so what's a good way to list or show open tasks where people can get into is there some pattern just do you create issues like it was code or The way that I've seen this done in two different communities is planning as an example create an issue for the event add tasks for the planning even if the planning is happening sometimes it's in meetings on Slack or it's a Zoom call just post a little summary of these people were in attendance this is what we did and usually at the end of the event you can say all of these volunteers were involved in the contribution of this event so before you close that issue just give them all credit in order to just recognize the work I kind of put the responsibility to do that with an event with the organizer list out all of your volunteers and give them contribution in a public space and not just at the event on a slide or in a meeting that they're in but in the space that everyone looks at maybe with projects we can use specific tags I know a lot of open search projects have an open for contributions tag but maybe there's also a documentation tag or a testing tag that needs user experience testing tag and that's a way of highlighting if you filter by these two tags you'll find opportunities for you to contribute that may not necessarily be review this issue, write me some code and I think one of the things to that is if you want to get people who are not familiar with the project is to use times they are familiar with instead of we have an RFP or something we need designers we need this so that I know how to do design I don't know anything about Kubernetes we need technical writers we need this to contribute so that people will be able to see things they can do and also detailing in the rhythmies of the project this is how you can come in this is how you can onboard this is how you can contribute to a system where there are certain people we have a couple of anonymous here there are certain people who just want to do they don't want their names to be out there so creating an environment for them where they can actually perform some work or do some things or just give without necessarily creating paper trees probably maybe due to policies in their work or due to some personal reasons also creating an environment where someone can just oh I'm passing by this yeah fix move on they don't necessarily want to be part of community or part of anything but they just want to do a move on so creating that environment where information is public information is transparency and also showing oh this is this thing that was done for example like I think the Kubernetes project they list all the contributors that contributed to the project at the end of the video and what parts they played in it so it's which is part of appreciation also so being able to show and document how people can help where they can help specific things they can do and the resources they can use it's very valuable in ensuring that you have more non-code contributors to participate and one of the things we saw success with last year was doing a documentation themed hackathon like we're only going to work on the docs issues and I think that was a it was a good positioning for us to be like you normally don't come to our hackathons because you feel like you don't have you don't know Ruby or Rails you can't you can't contribute to the code base this is a docs hackathon all you have to do is read the docs edit some docs update some docs that are out of date and so that was one way that I think we saw some success I think it still needs there's more steps ahead of that I think we can improve on that but when you ask the question that's what I was thinking of awesome thank you only five minutes yeah any other questions they need the time to walk going once going twice well if you want to talk one on one we'll be at the GitLab booth for the rest of the conference yeah thank you very much for listening thank you so much


Hi guys, it's Monica. Thank you so much for joining me. I'm going to share with you a couple of my August favorites. You know, I know many of us are not out there shopping and spending a ton of money and I know I'm fortunate that at times I have products sent to me, but like you, I am watching my wallet as much as possible. So I'm going to share with you some stuff that I've purchased, some stuff that I've already talked about that was sent to me originally and my feelings about them now and why they might be a favorite or not. So I'm going to try to keep the video short. If you're interested, please stay tuned. So by the time you're seeing this, oh my gosh, August is almost over or maybe it will be over. I'm not too sure when this will get published, but trying to think and reflect on August was a very, for me it was a very emotional month. Many of you know that my dad passed away in July and that I struggled with coming up with a eulogy for him and it's kind of interesting somehow. Two days before his memorial service, it just came to me. But nonetheless, it was an emotional, emotional month. So I haven't got not gone on this any kind of shopping sprees or anything of that nature, but there are some things that I love that I have bought that I will continue to buy that I want to share with you and I'm going to start off with my earrings. I happen to like jewelry. I have a lot of crystal. I have a lot of Swartz skis. I also have a lot of pearls. Many of you probably seen some of the pearls that I have. I just love pearl jewelry. I love silver jewelry. I love white gold. I love any jewelry. Anyways, a while back, I noticed or I'd watched a couple reviews and I noticed that Wendy from Life with Wendy was also a jewelry maker. So I went to her Facebook page. I checked out her stuff. I said, Oh, I really like some of this stuff. So I contacted Wendy and she put together a surprise package for me. I, you know, I mean, what I paid for the surprise package and what I got, what I got fire exceeded the monetary amount I paid for it. I was absolutely thrilled. She put, she had a ton of earrings. I'm going to probably enclose a little b-roll here so you can maybe see from when I did it. But she had, she had a ton of earrings.  It was just absolutely, it goes really nice with blue outfits. And anyways, I love the earrings. I love jewelry. I love everything about it. So that has definitely been a favorite. When I went to Bar Harbor on a camping trip, I, I brought a bunch of her earrings with me. And when I went into town or had an occasion to really want to put jewelry on, I put, I put those on. So my life with Wendy, I'll link her information. She's a fellow YouTuber. She's just a sweet little lady and I don't know if she's little. I've never met her. She could be taller than me. Who knows, right? Anyways, I will definitely link her information below. The other thing that has been an all time favorite so far, and this was initially gifted to me. I have no idea now how long I've owned these two pieces, but Julia, a jewelry company, reached out to me quite a while ago and they offered me some of their, to look at their website. First they, they gave me one piece and then another piece to check out something that I want. And one of the things that I selected was this wedding set. This is very, very close to my real wedding set. And I just figured I could travel with it. I could, it would, it would be nice. I wouldn't have to worry. I could leave my real stuff in a safety deposit box and I didn't have to worry when I went away. And then they reached back out to me and said pick another piece. So of course I'm a ring person. I love the rings. And then I picked this daisy, diamondy, geeseing ring. And so anyway, why this has been my favorite. It at least has been about four or five, maybe six months now that I've had these. I have, I don't have a dishwasher. The dishwasher is me. So I've washed dishes. I've been outside. I've been on the ocean. I took these to Maine with me, like I said. I have been impressed with how they have held up. So if you are looking for a piece, I'll link their website. I'm not an affiliate member. I think, I think maybe if you go through, I'll see if I can find Taz's link. Taz is maybe if you go through her link. But anyways, I really, really have enjoyed the pieces that I have from Julia. They've been beautiful, but more importantly, they have held up. Even my most vocal critic at times, my mother, never could tell that I was wearing a piece that wasn't my own wedding set. So yes, I absolutely love my Julia pieces that I have. Again, I only own two, own two, the two rings, but they have been a favorite of mine.  So anyways, I absolutely love it. The, the other thing I will tell you is that I have some, I think I have two of these that were actually sent to me. These are the fake buns. I have one that's got a little clip here. So it all depends on how I wear my hair. This one I sometimes pin just if I want a light little flinchy thing on the top. Is that a word flinchy thing? I'll just, you know, clip this on to the top of the ponytail on top of my head. This one is my favorite, I must say, because it has the long trendles. I can't remember how long ago I have, I bought two of them. I have one that's, one's still in the package, but this is my color very close to my bio hair. So when I wear my hair up, whenever I put my hair up and I have these hanging down, people just think I have my own hair with extensions hanging down off the ponytail. But I will tell you, this has been my I think my best look. I love, I love this look. When I wear it, I feel young, sassy. I just enjoy putting my hair up and then having this one wrapped around. And also I have, I have several without any of the trendles, but I will, I will say that my fake wraps or my, my bun wraps, my alternate hair wraps have been a true favorite of mine this month because I've been out and about a lot. And when I'm out and about, it's so much easier just to clip my hair back and forget about it. The other thing that I always love is my jade roller. I don't talk about it enough. I know I should. I have a couple of different jade rollers. I think I got this off of Amazon, but you can find them in TJ Maxx or Marshalls, but I love my jade roller. I will roll my serums in. I will, I'm allowed to use this an awful lot, especially the small part that I go right under my eye bags with. So my jade roller, I mean, gosh, it might be six dollars. I typically, after I use it, I've rinsed it off. I typically keep it in the refrigerator, but even if I don't keep it in the refrigerator, the jade stays cold. It really does. And so we're cool. And it's just a really, I really like it a lot. The, the other thing, and I don't even remember, I just bought this. I think I threw out the packaging, but I think this, I just got this in Bradley's. So I haven't been going to the nail salon, as you can tell. I've been trying to do and let my own nails try to get strong. I, I'm, I eat my cuticles. I bite my cuticles. When I get nervous, I bite my nails. Instead of my nails, I bite my cuticles. So I'm trying to really work on that, but my cuticles are a royal mess. But anyways, I haven't been going to the nail salon and I have missed having a good pedicure. In the early winter, I did a foot peel and I'm probably about ready to do another foot peel, but I haven't done it. But I have this. Now this is the grady. It's the metal part. This, this really works your calluses, but you have to be super careful. You can't be going all different directions with this. It's one way and back, one way and back.  I love the going barefoot. I love wearing flip flops and my feet, because I'm barefoot all the time, even going outside. I'm always barefoot. My feet really pay the penalty for running around barefoot. So I absolutely love my callus scraper. I think, I think gosh, maybe five bucks, six bucks. Walnut, Amazon. I absolutely love it. And, and it's usually stays in the shower. So, and that's usually when I, when I would do my feet, when I'm washing my body, I do that. So I love this. This has been an all-time favorite. And I just picked up this travel size from my hairdresser. I actually have a big bottle of this that I wanted to travel size. And this is the all-nutrient sculpting gel. This is a body amplifier. I think I've been using this and I buy it from my hairdresser. I do believe you can get it on Amazon, but I think I've been using this for at least maybe eight or nine years. Now my bio here is very thin, so I need all the help I can get. I put this on wet before I either blow dry it or before I use any kind of heat. I put this on and it really does help my fine thin hair get some body. Now after my hair is dry and I'm styling it, you know, that's when I might use my helium or an other spray that also gives body. But when you have super thin hair, you need all the body you can get. And this has been an all-time favorite and continues to be a favorite of mine. And I think my last favorite, I think it's my last favorite, my last favorite has to be the, I look forward to this every August and we go to Vahraba Maine. It has to be the sunsets that we see on Western Bay. This year, we didn't have a huge dramatic sunset, but I love sitting outside having a campfire and watching the sun go down. And as you can see by this little video clip, we have a beautiful, beautiful view of Western Bay. Of course, it's the Western, you know, so it makes sense we'll see the sunset, but we normally have an amazing back splash when that sun goes down. And so my all-time favorite is watching the sunsets in August. So, yeah. So those are my favorites aside from every single one of you who watch my videos and support the channel and all of my YouTube friends and ladies and people that I feel really connected to. You're all my favorites. Those are some of my favorites. So you all are included in that as well. Anyways, thank you so much for watching. If you have any questions, comments, please leave them below, thumbs up the video, and subscribe if you haven't. I truly appreciate every single one of you. Bye, guys. Oh, I forgot. Wigs. Yeah, wigs. Yeah, my wigs. I should have, I should have talked about that when I had my, anyways, on my head right now is a wig that was sent to me complimentary from Wigs Studio One, which I'm going to do a review where by the time you've seen this, the review might already have been out for their channel, Wigs Studio One. This is Henry Magoo's Farrow wig and I, this is such a realistic density and style. I absolutely love it. So this has definitely been a favorite of mine this month. Now I think I'm done. Bye, guys.