 Hi, welcome everyone to this online edition of Open Belgium. I want to quickly thank our three main sponsors, Mono Design Microsoft and Agentschab Binalands Besture. But without further ado, I want to give the floor to the speakers of today. Great. Thank you, Astrid. I'm going to pull up our presentation and we can get started. So hi everyone. Thanks Astrid. Thank you Open Belgium for having us here. And welcome to you all. Thank you for joining us. We have some new ideas and we're really excited to engage with you all today. And some of the websites we actually will share at the end are quite literally hot off the press. So this is very exciting. A few reminders first on this new platform, Big Blue Button. It's new for us, maybe for you. First, we have the video and microphone turned off for all participants right now. So you don't have to worry about it. But we will allow you, give you the option to turn it on later for discussion. Also, the platform on your end might look like a dashboard right now on your screen. This main presentation will remain steady if you choose to drag and drop the boxes of the presenters. That's me and Gulshan and the slides that we're sharing. So let's get into it. So before we jump into these real juicy topics of open data and social justice, we can introduce ourselves. We're here with Civic Software Foundation, which is a U.S.-based nonprofit with a global network of collaborators. That's why we're here with you all today also. We create technology and applied practices that reinforce equity and democratic values. So my name is Anjali Mehta. I'm our Chief of Staff. And my name is Gulshan Kudar and I'm the Director of Research and Learning. Great. And hopefully we can spark some new ideas today and we'll have full contact information at the end. But in the meantime, please use the general chat amongst yourselves. We unfortunately don't have another type of moderator, but we will try to track the questions and keep time at the end for them. So let's get to it. All right. Well, let's today start here. We hope that everyone joining us today came to open Belgium 2021 based on some fundamental shared understanding and something of our event description, spark some ideas and interest. So we hope that you have some understanding of the larger open data movement. It's good intentions and it's a fake ineffectiveness. The growing body of scholarship and debate over and debate over criticisms and solution oriented innovations. So what do we intend to propose as a remediation and why? Let's get into it. So what is open watching? Open watching is actually the main problem we'll be tackling today and build our discussion around it. So we define open watching as publishing data for the sake of having evidence for data driven decision making or creating a transparency agenda with no context around data or planned use cases. And with little to no consideration on implications and embedded biases. To start, let's examine how open watching is mainly the effect of two well intentioned but ineffective motivations for open data. First, transparency. So the idea of transparency within open data movement has more often be presented as a one stop shop fix. You can imagine the logic. Look, our data is published on our public website. So we are transparent and we are accountable. Sometimes this even leads to the measurement of accountability based on the amount of public data sets that are openly shared. We know for a host of other reasons that even if all the data sets were published and made open, it still won't be a clear picture of what happens inside of an organization throughout data's life cycle, decisions around data, and how their practices served. So we tackle we understand there's the first line of intention transparency and the second one is data and technology determinism. I believe everyone is familiar with phrases such as the numbers speak for themselves or all you need is data or data has a better idea. So these all signals the wider cultural connotation data that cultural connotation that data is objective and truthful and inherently valuable. So data driven decision making is actually an outcome of this idea that data driven decision making. So that's why it's called. So data is something accurate and is something accurate than anything qualitative and that because of this, the resulting actions are just the way things have to be. Understanding and the notion of data driven decision making also feeds a real and palpable excitement to quantify every aspect of human life and experiences. Over reliance on and deep trust in data to understand issues to expect data to show us what's wrong and make decisions is a pernicious and cynical problem. So you see on one hand we have transparency and on the other hand we have data determinism and these two strongly connects to social justice and in actually circular problem. So let me explain what we mean by this circular problem. The first the cycle starts with data sets published and made open with no context around the decisions motivations or processes along the way. This is what we call open washing. Second step then comes this process leaves data constituents, people who are presented in or impacted by a given data set. So these people are left out of this data life cycle or any decisions around it. And then what happens is they turn into resources for data. Then this brings the next step, which is giving no context around data sets obscures the bias nature of them, which then leads to data sets being used as piece of evidence in defining the problem and agenda setting. Well, no context around data then leads to defining the wrong problem with bias data sets, which then causes unjust and biased resource allocation, causing real life harm to oppressed and marginalized groups in society. So there is no real accountability around these decisions and even no requirements to be accountable because it's data driven decision making in the end. So this leaves the questions that rise from unfair data implications unanswered. The logic of more data set equal more data equals better understanding is being used as a metric of success for these so called decisions. So what happens then in order to measure the success of these decisions more data is being collected and then published again with no context around it, which then starts the cycle back. It's hard to imagine how deep this cycle is because it feels like it's how we've been working for the past many years. But what Gulshan has been talking about is just a bird's eye view of a large body of scholarship and research on this topic, or certainly not the first ones to call this out. But with this foundation, what we really want to get into is what actions we can take. How can we address the heart of this problem? How can we sustain the movement and really create a bedrock for lasting change? Across leaders in the field, all research, academia, research, think tanks, there is a resounding consensus, in theory at least, for a purpose driven approach through new collaboration networks. Specifically when we're looking at these problems of open washing, of transparency, of data determinism. You have think tanks like the GovLab in New York University and the Associated Open Data Policy Lab, expounding the idea of a third wave for open data. There's a lot on this third wave and I encourage you in another place to go research that. But all that they have to say, they identify the needs to address these problems in three main points. One, that publications need to publish with purpose. We need to foster new partnerships and we need to prioritize data rights. And they call out repeatedly across their publications, this focus for context, and I'll quote from one. This latest iteration of the open data movement concerns itself with a technical, social, political and economic context in which data is produced and consumed. This is quite resonant with arguments in data feminism, a book published about exactly a year ago from Catherine Dignacio at MIT and Lauren Klein at Emory to major inspirations for us. But in this book, they also have a call, a specific call for action for context. But they also acknowledge that not having the right incentives or networks make it really difficult for this context to be created and to really happen. So I'll quote from their chapter literally titled The Numbers Don't Speak for Themselves. They say, governments and data providers have not invested as much time and resources in providing context to end users as they have in providing data. And data sheets for data sets are great, but can we expect individual and small teams to conduct an in-depth background research project while on a deadline and budget? So we have come to conclude that this purpose-driven approach that is meant for creating context in the collaborative network that will create it needs to be established outside the current data lifecycle. The question is now, how do we do that? Who should act and how? Now we're really getting into why we brought you all here today. Based on what we heard from Anjali, then we can define context as the situated knowledge in which a data set was produced and abstracted from through collection. In order to break the open-washing cycle which I explained earlier with specific regard to creating space for social justice, we need context. And as civic, we offer a methodology, structured context. Structured context is an applied methodology to remediate open-washing effects with inclusionary practices. Social structured context itself is a technical metadata schema designed to address these two main critiques. To address the pseudo-transparency, structured context focuses on commitment and trust. And to counteract data determinism, we put lived experiences of data constituents at the center throughout the data lifecycle, capture what has fallen through the cracks of data categorization. This quote from Howard Zinn reminds us once again why we need something more than data and why data is not answered to all of our questions. It reads, statistical historians have tried to assess slavery by estimating how much money was spent on slaves for food and medical care. But can this describe the reality of slavery as it is to a human being who lived inside it? Can statistics record what it meant for families to be torn apart when a master for profit sold a husband or a wife, a son or a daughter? So, given that we have this definition of structured context that we now want to apply with a methodology, we can talk about how to really build this. So the collaboration that builds these inclusionary practices within structured context is recognized through iterative cycles, engaging stakeholders and constituents that are both inside and outside the normal data lifecycle. Pictured here is a simplified form of civics infinity loop collaboration model where we highlight that energy continuously flows through the system. We'll get into the different actors and stakeholders that lie along this path. But first, let me explain exactly what type of context we mean, what specific questions make up structured context. But based on the calls to action from all the previous scholarship and research we are building that we're building from. We've identified and created two main buckets of context to form structured context. And that's where our collaboration model is focused on. The right hand side of the loop is what we call foundational context. This is how we understand and capture the production of the data set. Essentially, this context is answering who collected the data, why they collected it and how. The left side of the loop is transformational context, alluding to the cultural shift that can occur once this practice of structured context is in wider use. This context goes beyond the data lifecycle to capture the resonance of a particular data set. This is where we break ground on the deeper situated knowledge of lived experience. This means engaging with data constituents, as Gulshan defined as those who are represented in or by a data set, as well as other domain experts to explore the social justice context, historical context, and limitations and use cases in regard to ethical governance. Specifically, this can mean things like questioning the potential harm caused by a data set, the historical connotations for certain categories, and for which uses applications of this data set is best used or not used for. So to really explore how this ecosystem will function, we want to hear from all of you. Our collective discussion in this next few minutes will then lead us to a fuller group discussion that will lead to further engagement. So we will be splitting into breakout rooms in a minute, and we'll all be contributing to the same matrix on a shared board. So the purpose of this is to first understand the different types of people involved in that infinity loop collaboration model we just described. So we think of actors as those who will be involved in creating or contributing to context in some way, either foundational or transformational. Stakeholders are those individuals or groups who will benefit from this type of context being created and distributed. And to be clear, we are not advocating for the actual data to be shared any further or more publicly, just this resulting metadata that's created from capturing context. We want to understand how these different people relate to the different levels of context, and the stickies that you'll see in the shared board are placed to brainstorm who, what, and why. So the way this will work, we get off to the races. Through smaller group discussions of five to six people, we want to imagine a better world where we are each civic agents connected to this larger community. We hope we can allow yourself to brainstorm and dive in here without holding back on constraints like funding or resources or scale. We have some ideas there and we'll be talking about that later. So let yourself imagine. The goal for your group is to collectively complete the template, at least once per quadrant. And don't worry if your notes and the stickies match the grammar of the template sentence. Other just logistics for these breakout rooms in this new platform, you will be automatically placed into a room. You don't have to worry about that. You might be prompted with that echo test again that you saw coming into this main room. And again, when you come back, when the breakout room ends and you come back to this main presentation. But other notes, the general chat will stay open to communicate amongst yourselves, as well as private chat to meet a cushion for assistance. You'll probably saw more buttons at the bottom of your screen. So your video and microphone can be turned on once you're in the breakout room. And last, the cushion shared the link to the mural board in the chat. This is a shared brainstorming space. And this is a direct link. So you will not have to sign up for anything. It will open up in your browser directly sending you to the board where you can edit as a guest. So any quick questions on logistics? You can type them or ask. All right, then. Well, I'll have Gulshan send us off. We'll be jumping into some of the breakout rooms and keep the conversation going. So looking forward to it. Let's go, Gulshan. I see there are people joining in the breakout rooms. Oh, great. We have three rooms. So we have one people in two breakout rooms. Astrid, are you still on to help? Okay, now more people are joining. And if you want me to join a room, I can also join now. If you want to join a discussion. Yeah, sure. I'll wait a bit to see who here seems to be alone at the moment. So we can move him to room two. Yeah, I would do that. And maybe then I can join room one. How do I do it? I think for me, I can't see who's in which room. Or if I join room three and tell here to go back to the main presentation and then be reassigned. Okay. Let's try that. Okay, I will join his room now. Okay. People are in the mural board, so hopefully it worked. Our timing is really good. Yes, I can invite people who are not in a breakout room. I'll add to a sticky make sure people are going. Okay. I'm back. Yeah. He left the audio, but okay, he's in room two. Okay. Now I see it. Is it okay if I join room one? Yeah. Okay, see you later. It's nice I can send invitations to people. Maybe we can join. Maybe, okay. One of us can stay here and then one of us can visit rooms. So I can go for five, ten minutes, then I come back and then you can go. I'm just on the mural. There are adding and I added a few. I added two just so it looks like. That's good. Okay, I'll go see what they're doing. Okay, have fun. Perhaps you're not actually in a breakout room. You mean you have to be in a breakout room here as well? Yeah, so let's see. This is the main room and they're in breakout rooms. Let me see if I can help get you into one. Thank you. Because I clicked on the app from mural. Oh, that's good. On your left side, do you see like where the users are listed? I see users, shared notes and messages. Oh, okay. So we don't see the breakout room. I see Gulson, her name. I think I can get you into the room. This is a new platform for us but we should be able to get you into the room. Thank you. I understand that it's hard. Complicated a little bit sometimes. It's an adventure. Yes. How did everybody on board in the second room? Maybe by explanation. I'm not sure. I'm back. Okay. Do you want to join room one? Astrid says they're a bit stuck. Oh, okay. Or if also trying to get me into a room. Hey, Gulson. There's only five minutes left but it can still be fun. I'll just wait until everybody returns and I'll see the results. It's fine. Okay. Thank you. Gulson, do you want to join room one though? Because Astrid asked. Okay. All right. Are we all back? I don't know how long we have left. I think we'll show the rooms ended. Yeah. Okay. No warning but I'm glad that worked. Great. Well, welcome back everyone. The mural board seemed very active. So thank you all for participating in our little activity. But let's come back together and see where this has brought us so we can So before we get into civic software sharing want want to hear from you all. So if you're comfortable still using your microphone and we welcome video to if you want. Let's just start off with some top of mind thoughts or reactions. How did you see your these actors and stakeholders interacting around the what we call the infinity loop model or any anything that comes to mind from what it felt like to go through this exercise, whether it was difficult or easy. You are all unlocked by the way. So you can just unmute yourselves and come on camera if you want. Hi, Lucas here. We had like the great help of Gulson. She came in and explained to us. We had like some yeah doubts if we understood correctly. And it is interesting to see that you're using this picture with this like loops. So it seems like everything is interconnected and depends on each other. And in this graph that we worked on on the my row, there is like this hard lines. And it seemed like, okay, there is the different shape, right? It's like, if I'm here that I'm not there. And I would like to raise a question that is it possible that basically you have to go through. There is the need of a foundational existence before you can even consider to get to a transformational one. Is there this dependency? I think it's interesting question. So when we can get into how we envision this program really working. And from a mental model, I think it's easier to think about if we have the foundational context and we understand where the data came from, then we can think about its historical context or previous connotations. But when we actually get working, if once you kind of are in conversation, I don't think that's literally necessary to have one and then the other. For example, engaging with, you know, we have term data constituents. Those represented in or by a data set might not be an individual. It might be a community organization or someone who has studied these trends before as an anthropologist or historian. And so engaging in that conversation first, or not first but simultaneously, can also help the foundational context workers, whether that's a data steward or a researcher kind of answering these questions, think about it more critically or open up new conversations that they hadn't previously thought about. And then answering questions that are quote unquote simple about how they collected data. But I hear your point that when we put the graph together to help make a simple brainstorm, how it also conflates the interactions and the collaboration in real life, humans talk to each other and don't have clear lines between them. I don't know, Gulshan, do you want to add more to that? We talk about this a lot. Yes, I think the concept that just popped in my mind is intersectionality because we do we as humans, we have multiple identities and multiple roles in the society. So for instance, you can be a data scientist, but on the other hand, a transgender person who is being influenced by the data decisions. So for instance, if data collection only has two genders, and then just a box saying other, then for instance, who decided on these categories, why there was this need for these categories can be a part of foundational context. But then when you dive deeper into this binary understanding and kind of surfacing this mentality around it, then it can, you know, slowly moves into transformational context. But first you have to identify the categories and the way data was structured and decisions. And then having that foundational information, foundational context, then we can move into transformational one. Yeah, I'll also add just one more and maybe other people have questions from it. And kind of that linear thinking we need foundational for transformational. But there's also like once you have that transformational context, you can answer even more questions about the foundational context. And so that's how the loop kind of keeps itself in perpetual motion. For example, we talk about foundational context as Gulshan mentioned understanding these categories and what it really means. That also alludes to privacy and security or ethical use in terms of limitations. And when you start seeding those conversations, when you get back into foundational context about how or who collected this methodology, it might open other places where you didn't think to document this before, but now that this question has been brought to the table, we can add even more context to our foundational side. Once questions and conversations have been opened by these transformational questions and conversations. So thank you, Lucas, for that dynamic question. Any other top of mind thoughts where these type of actors and stakeholders had you had an opportunity to brainstorm their interactions together before or emerging things that you may have seen when trying to think of stickies in each of the four boxes because we made you. Yeah, I wanted to share because I'm a data scientist and usually my work can be very isolated depending on who I'm consulting with. And it was really nice, even though we had the grids, it really helped me see like the whole picture very succinctly. Like, maybe I don't know exactly what the project manager in the company does, but being able to see what they, right, like their motivation and how they're part of each step in the process was really useful. Because when I saw what other people added, I could go and reflect back to what my role can contribute to what they're saying in the, right, what they say, oh, I want to help with this context. And I hadn't thought of that context at all. It's really nice to just see kind of the layout on the land and then go back and think how I can contribute to all those spaces. I can't, right? Maybe I can't, but like, it was really nice to, I could see the infinity sign at the beginning when I saw the grid. I wouldn't have thought of the infinity sign, but that's what happened to me at least that I kept going back and forward because I wrote something in the first block and when I got to the end I was like, wait, this relates back to the first block so then I would go back and forward. So I thought that was really cool to see. Thank you for sharing, Vanessa. And thank you again for joining if you have to believe. But I think that brings up a lot of good points. We have these roles and that's, you know, how so many of our businesses and society have been brought up how the open data movement functions. But it's almost hard to get this step back to look at what we're really doing. And so I wonder, kind of Vanessa, you helped me bring up in going through this grid system, even though we had this infinity loop model on the side. Any thoughts about the power dynamics between these different actors or types of context? We didn't ask specifically in the grid, but I wonder if having gone through that exercise, if it's bringing up any thoughts there. And I also would like would even augment that question how power dynamics allow or don't allow someone to exist in any of these spaces if any thoughts are there. Or no answers, but does this make you think of any other questions? Yeah, I guess like I hadn't even thought about that right because when I was in the grid I went rapidly to the foundational first grid. So I was like, well, this is where I have quote unquote power. But then I didn't really question, right? Is there other places that I could be contributed and I don't because I don't feel empowered to do so. And I bet it happens, you know, in combination with all the grid components too, right? Like maybe a private manager doesn't feel like they can contribute to the data acquisition process. But in my experience, that's not true. It depends on like the conversations you want to have. And not even nice. Like if we think about individuals and data owners and, you know, where is your input? And I don't know. It's a very great question, but I'm just kind of not sure what that would look like differently. I'm just stuck in the grid, I guess. Well, that's why we're here experimenting with a new model and proposing a new methodology. It's because these questions haven't been asked or really dealt with before. I think the whole point for us, for this even new methodology is to realize that there are questions we can and we should ask. Because of most often when we are really getting into this concept of predefined or this trends or this culture around data, we often just adapt to that thinking system. But with this methodology, a new way of thinking, we really want people to question and to think about what questions even can be asked. And we, of course, acknowledge not everything can be answered, but even the very act of asking questions is something amazing itself. Great. Well, thank you for your thoughts. We have a few other things we want to share with you. So we'll go through that and then bring it back just to open questions. So we discussed and throughout this session, we first recognized the problem and then identified two main factors that was feeding into the notion of open washing. And then this was important to acknowledge and identify this process so that we can have a clear understanding on what needs to be done, what causes this problem and how we can think about solutions that really addresses the core of it. And which then this grounded understanding would bring visibility around this topic, like places like this, when we get to talk about the possibilities or problems, starting this discussion and then having the visibility on these topics, that would allow us to collaborate and get action and move on to this active part of actually changing the things. So not only complaining, not only addressing the shortcomings and pitfalls of the current systems, but then moving to the active part and thinking, what can I do? What can we do to change it? What other possibilities, imaginaries we can think about? So we offered an intervention, a structured context as civic, but we acknowledge that this method itself will not change the world. And for it to work, we need to create this ecosystem, including many actors and stakeholders, active participation, commitment, and open to be questioned and to ask questions and get comfortable from time to time and think about really our own positionality in this process as well. And so what we offer today is a place to start and an invitation to have a conversation. The connections seems to be lost. Yes. She should come back in a second. What she was saying is that we have this model to start a conversation and we call it the flower of hope because we're very optimistic and we think if you plant the right seeds, we can bloom into a beautiful garden together. But the point of this visual and this model is to have a clear vision of what structured context can do for us and how we can build it together so that you can bring in different levels of trust and commitment through visibility. So that's one of our pedals here and the agreement of this visibility. It's really bringing to life and shining a light where it's often hidden the structured nature of, constructed nature of data. And so we have this model to highlight the collaboration of what it would take to bring visibility to these different points in an open environment that allows for questioning without shame. Gusion, is there anything else you want to add to this flower of hope? I think I picked up where you left on and off. Sorry, I dropped out. But yes, the point is we really try to stay away from this mentality that here is one thing we created and it's going to fix everything. But we do have to think in a systemic level. And we do have to approach this issue of open washing and in the end, social justice as an active action and not just in silent efforts. Yeah. So with that, and all these new ideas of what we're really trying to, you know, engage everyone on, want to share this very new thing. So first, again, thank you for participating in our quick but lively brainstorming activity. As we mentioned a few times, you know, we're not the first ones to call out this need for attention remediation. But we do have an actual practical application to implement this methodology for structured context. So big news, Civic Software just launched a new program and call for participation in building out what we call the Civic Data Library of Context. And this is going to be a novel digital public resource to host a full catalog of contextual metadata, all created through the inclusionary practices that we've been discussing around the infinity loop model. So if any piece of today was at all interesting to you or sparked new ideas or questions, I invite you to follow our website to learn more and find ways to further engage and contribute. This is a wide call for participation. We are looking for individuals who want to actively participate in grounding themselves in this new practice of structured context. This program called the Emergent Lab is a place for applied learning and talent development. We're also looking for different organizations who share the values embodied by structured context. Organizations whose mission in some way, shape or form fits in with everything Gulshan and I have been talking about today. And these organizations can become implementation partners with us and implementation partners means a few things. It could be any or all of getting training for internal competencies can mean going through the applied process of contextualization and publishing the resulting metadata in this new library product. This means studying a foundational framework designed to empower the full teams and illuminate at a wider breadth the invisible human work that goes into creating data ecosystems. So again, more details on this website. And I'll leave you here with all of our contact information. The website again, a feedback form if you want to do it now also email it out. We have a few minutes left. I know we're at the top of the hour and it's scheduled past and we want to open it up to more questions either about our program, anything we've discussed today. Any other questions that may have been sparked. Yeah. Any questions anywhere. Otherwise, again, big thank you for joining us. It was really exciting for us to be able to share and engage with you and you already brought up so many good points for us to keep thinking on both in your questions and in the mural board. I have a question about the library of context so you want to. What's the ultimate goal. The library is to create this new product in this new visual representation and actual resource that captures this new form of structured context. And so we're taking from the beginning those quotes and from data feminism recognizing that there's no data intermediary or, you know, current actor in our data lifecycle system that can take on the accountability and responsibility to creating this context. And so that's why civic software foundation is setting this seed so that other people can come in with a structure and infrastructure and funding setup. But creating this place in a repeatable way to create structured context. And so the goal of the library is to one make it easier to see what context can do. It will help share this metadata in new forms. It will get more people engaged in being able to ask these type of questions without hopefully, you know, feeling the shame because it's not yet trying to fix anything. It's just trying to make visible these new questions and conversations that we can ask. So it's both a product to share a catalog of metadata and the program that is going to create this product is a true inclusionary practice bringing in a lot of different people for experimental learning and emergent creation. Okay, cool. We'll definitely check it out. Yeah. And I think it's a good reminder here again that we don't, we don't, it is not a requirement to publish the whole data set to be participated in the library. And so that all that sense we understand there are some regulations and of course organizations are really concerned about, you know, crossing the line and afraid of doing something illegal. So in that sense, we, we advocate for context data and say that we don't even have to see the data itself so data points itself but just the categories and understanding the processes it went through would be a good start for creating context. Well, leave it here unless there's any more. I welcome everyone to this.