 Okay, we have the Visible Wikimedia Lab, Fostering Multilingual and Decolonizing Structure Data, Narratives on Wikimedia Commons by Who's Knowledge. Okay, let us welcome them and kindly use the hashtag that we have for today's event, Wikimedia Summit, and share sharing is caring. So let us share our hashtags and our photos and whatever inputs or insights that you have on the summit that we're having today on our social media. Thank you very much. The floor is yours. Thank you. Hello. Thank you very much for your attention in this. We will do a talk, but also we will put our hands on on Visible Wikimedia Women for some minutes also so be prepared, have your laptops and cell phones at hand because we will do some a little exercise. So next slide. Can we manage the slides? Oh, yes, with this. Women intake. Yeah. Women intake. Good morning, everybody. My name is sunshine Fiona comes on. I am the Visible Wikimedia Women Campaign coordinator. I'm an African feminist from Kampala, Uganda. And I can use this. My name is Mariana Fosatti from Uruguay. I work at Who's Knowledge as the decolonizing Wikimedia Program coordinator, and also with my colleague, Sunshine, organizing Visible Wikimedia Women every year. And next slide. Okay, perfect. Is the red one? Ah, I got it. I got it. Okay, so we'll tell you a little bit about Visible Wikimedia Women. I didn't describe what I'm wearing. I am a black woman. I am wearing a bright orange dress by a Kenyan designer called African Uva. And our work at Visible Wikimedia Women is about bringing images of black, brown, trans, indigenous women from mostly global majority countries, the global south, to Wikimedia Commons. We have been doing this work for the last six years since 2018. And we have together brought more than 8,000 images uploaded to Wikicommons and we are still counting. And we have done this work. It's very collaborative work with feminist organization, collectives and movements, and different editors and groups, and we have been able to do this with also different community organizers, and we'll be continuing this campaign hopefully for many years to come. Yeah. Next slide, please. So today, we will speak not only about the images itself, but about data and information you can find on Wikimedia Commons each time you see or you upload or you reuse an image. And one very critical piece nowadays in Wikimedia Commons is structured data on Commons. I am sure that some of you have heard about this, but maybe not everyone or not everyone have had the opportunity to explore structured data on Commons. So let me just do a little introduction. With structured data on Commons, what we can do is to link or to match images and the content and the very meaning of each image to Wikidata items. So for instance, if you and you can see some examples there, a like bubble or a book or a computer or a feminist strike even can be matched with its queue element on Wikidata. And in that way, we are giving signals to machines to and we are in certain way, saying what's the meaning of the image and what elements are in the image. And so many different kind of things can be leveled this way. I can, as you can see from, you know, books, computers or historical events like the feminist strike of 8 March 2018, for instance, that already have a queue item on Wikidata. And by doing this, we are making images machine readable, but also humans, people can retrieve images in a more nuanced and effective and satisfactory way, hopefully, but we also want to point some problematic and critical aspects of this feature that we have seen in visible through the thousands of images brought to Commons through this campaign. We can start experimenting and making some observations about how the plurality of women and womanhood is represented on images and also on data. So we can start, we are trying to understand issues around visual and that data gender gap, accessibility because through today data on Commons bring accessibility features to Commons, multilinguality because every item on Wikidata, it is potentially multilingual. And also, we have starting observing the biases that are present in automated tags on structured data because the structured data on Commons offers an experimental tool since several years now to get automated suggestions for tagging images. But our question is this one. Does the data tell the stories behind the images? Does the data tell the stories? We know the context of that is part of the image, the narratives, the different narratives because not all the nouns, not all the concepts are the same or have the same level of images are very, it can have very different meanings depending of who is interpreting the images, what's the interpretation of the images. The semantic of the images can write a lot and also the narratives. So let's share some stories about images. Yes, let's do that. So, when I mentioned earlier that we are work visible with women is about bringing images of women, we should, I should also have mentioned that we are not very attached to the idea of womanhood. And that also our idea of visible women is also to encompass non binary people. And this is an image of George Kizzi who is a non binary Ugandan artist, makeup artist who uses fashion and art to be able to push the boundaries of what it means to be someone who is in fashion, what it means to be someone who can use makeup and really pushing the boundaries around gender and around, around binaries through, through clothes and through makeup. And when we look at this image, when we look at that, that this image particularly does not have any structure data that is attached to it. And the idea of bringing this, the stories like this is we want to be able to create the kind of data that we want to show the plurality of our human experiences, especially as people who standard of humanity or standard of who is a person who is a human being does not fit within, within colonizer within our hegemony structures of what it means to be human. So that is part of our decolonizing project to be able to expand our the understanding of data and also the understanding of imagery in terms of what is represented and what tells our stories. So this is another image on the visible wiki women 2023 season, I think, and once more we will have this image is a union is from Brazil, but for instance, in the file name, we don't have the real name, we don't have, we don't know if if she is willing to share or not the name, the real name. So in the structured data section, there is no wiki data item. There is the captions are not really multilingual, there are not many information. So sometimes photos are just taking in big events and shows uploading in mass in in batch, and especially when these photos are about women, women from from the global society and the context can be so important that the image is itself. We don't know, for instance, if she is a candidate, if she's a union leader, and all that things make women visible too. And this one is another one from visible wiki women 2020, I think, she is Sarah Maldoror, an Afro Caribbean filmmaker, she passed away in 2020, because COVID. And this is a photo that actually is illustrating her biography on Wikipedia. And this one is very good described in terms of a structured data, because probably because once she died, many, not that much information, but more information was available online. So you can find also wiki data item about her, which is not the reality for the previous one and for many others. So, but one interesting thing about this image was you can see that she's a woman with a microphone at hand, right? So, and there are some structured data items related with this picture, including Sarah Maldoror wiki data item, including a microphone, including a wingless, including elements that are like this dissection of the person in different parts. And we wonder, is this meaningful? This kind of very detailed description is all right. It creates or it represents the story that makes justice to this woman and her very important career as an independent filmmaker. And one curiosity about this is that if you visit, there is a link in every wiki media comments page called information page. It's not very visible. It's like a behind the scenes page for every image. And in that information page, you can find more metadata and you can find almost at the bottom a section with the suggested tags from the automated system for suggested tags. That tool is provided for a partnership between wiki media foundation and Google for images recognition. And you can find different potential categories or tags actually for describing this woman. And one of these is art musician, art music artist, spoken person. So we wonder how this image recognition system can create or suggest tags that really misrepresent what you can see in the image. It's true, it's a woman with a microphone, but she's not a musician for instance. Every woman with a microphone looks like a singer or like a music artist, or specifically black women look like music artists. How many men look like this for the machine, the automated image recognition. So we are just documenting these problems and our whole consideration here is the politics of images and data sets. And let me share this quote from Kate Crawford and Trevor Paglen from excavating AI, the politics of images in machine learning training sets. It's an excellent article. The whole endeavor of collecting images, categorizing them and labeling them is itself a form of politics, filled with questions about who gets to decide what images mean and what kind of social and political work those representations perform. So our invitation is to have this question in mind, this critical question in mind, every time you see an image, you tag an image, or you decide to not to not tag it in a space in certain ways. And this the here come our exercise. So for this particular exercise, having we hope we've fired you up a little bit and made you feel like doing work on structured data, especially for images of women and non binary people is revolutionary political work. And we invite you to scan our QR code, which will take you to the visible wiki women category on wiki media commons, and to find any images there under that you can be able to add structured data to right now. So, whatever image you feel like is lacking in structured data or you want to be able to add structured data to it. This is an invitation to do that. Yes, that's the QR code open. Okay, great. Take like five minutes to find an image and look not only the image but the structured data in what language is the structured data is a wiki data item about the woman there. What other suggestions you can you find the the automatic suggestions or not what those suggestions say things like that. Do your analysis and then we can start the Q&A and we want to hear your observations and your stories about these images. You can speak between you. One another. This is a workshop path so you can be noisy. Speak to each other, speak to your partners, show to each other what do you see in structured data. If you need, we can also try to show you in on screen also. Are we able to open it? I don't know but maybe we can try. I think we can. So let's go. Like in 25. Oh, okay. Do you want to express it? Ask it to the link. That's what we're trying to get them to do. Yes. To open the link. Thank you so much. Turn your head if you don't want to be on wiki. Oh, yeah wiki women. Wiki women and allies. Thank you. Thank you allies for being here. Thank you for your support. And while we're waiting, while we're doing this exercise, I want to give a big cheer out for what's happening Saturday. Wiki women lunch historic. So this is where the QR code would lead you. So our ask is that you go select any of the images in the category of visible wiki women. And just like check the structured data below that image. And our invitation is to, you know, make observations if images don't have data. Also for you to be able to add any structure data that you that you feel could be useful in interpreting that image as well. So really that is the goal of this particular two minutes that's left of this exercise. So there's multiple images. Could you scroll down? I don't know if scroll down but English is not my first language. So just look at some of the amazing images that we have uploaded and yeah, check the structured data. Also no pressure. I think our five minutes are up. Does anyone have some things to say or just like share an observation share feedback about your experience. Like I said, no pressure, but don't give me zoom silence. I would appreciate that very much. Thank you. Anything anybody. So I'm Lucy. I'm from England. I'm a dark haired white woman. And so I've been adding some data to a page for Anna Monterosa. The level. Yeah. Who is a very famous Uruguayan woman and I'm very embarrassed by my extremely English accent. But what's interesting is I've been doing it on my mobile phone. So it was really easy to like add a description. There was no description of this image that had been uploaded as part of the program. And that was really easy to do. But when it came to adding categories on my mobile phone, that was much, much trickier. And I just think it struck me that there's this disparity in how the tools that we usually use, you know, on our computers and laptops work and have different functionality on a mobile phone. So that's another, I guess, kind of something to think about when we're designing these these programs, perhaps with people who don't have access to laptops. Thank you so much. I had a really interesting conversation with Mariana, which was looking predominantly at the African woman, a woman in Ghana holding a bag. Where there's almost an invisibilization of people in the images and Mariana was so gracious to explain to me that sometimes there's an active preference to not be named. But oftentimes, you know, you're you're engaged, your pictures taken, you end up on the internet, but you're completely invisible. No name, data, no context of the picture. And I thought that was really powerful as something that we could disrupt, which is inviting people to share in their agency on the internet. Thank you so much. We'll take one more. Anybody. Okay. Great. Thank you so much. I still have things to say, but we appreciate the applause. So we thought that we could end by just talking a little bit more about the campaign. And Masana gave me just the language right now about this being not just political work, but also work of not just feeling gaps but also correcting rhetoric or apparent injustices or invisibilizations. And so our campaign this year, so every year we have a theme is on body plurality. So all those other hashtags are body plurality in different languages, Portuguese and and Spanish and also look as we also would like to continue practicing also multi-linguality within our work. And we are celebrating the full uniqueness of our plural body sizes, shapes and identities online. And one of the very important things that we did when we're thinking about this theme was reading feminist theory around what it means to be a person with a body, but also to be a person with a body in a world both virtual and physical that says beauty is one standard and beauty is one thing and being visible means one thing. So we read stories about black athletes, for example, like Casta Semenya and what World Athletics Authority have done in terms of violating the sacredness of bodies that do not conform to a particular standard. And we read stories of Indian athletes and we were able to be to contextualize what body plurality would look like. And also our invitation for visible women this year is to find images that are of different body shapes, colors, you know, I like color, if you can't tell. And our campaign goal this year which started in April is to bring 2,500 images to Wiki Commons and other visible Wiki women 2023 category. And that work is not just about, you know, quantity of images is also politicizing why images do not exist. And we do that with care and consent and ensuring that we get the names like we ask the people, for example, whose images that we take. How would you like to be identified? What are your pronouns? From what part of the world are you from? Which place do you call home? So that that information, because we would like to see how much structured data travels in terms of informing image interpretation by machines. And the part about, you know, care, practice consent, I have, we have an interesting story. So I was part of the changing faces changing spaces conference, which is pan African LGBTI queer movements organization. I mean organizations, organizers, movements and collectives. And we're very excited to be part of that. One of the things that we do is photo booths to go there and take images of all these amazing African queer organizers and get their information, how they want to be identified, the kind of work that they do so that it is part of their descriptions. And when I came when I went back home to Uganda, the week after, well, Uganda has been leading this anti LGBTI movement for from behind for a long time. That's that the week that the president of Uganda sent it to the anti homosexuality law. And one of the things that we quickly had to do was to go back to the Ugandan queer organizers that we had, whose images we had taken, whose consent we had received to say, would you like, would you still like us to share your images on wiki commons? Because this is what it means when your image is out there and would you still like those descriptions and identifications to be up because we want to be able to also understand that it's, it's important to be visible. And sometimes visibility is resistance, but also anonymity is a form of survival for many people in different contexts, and to also think like start thinking what does it mean to, you know, ask for speedy deletion and take images for example at events in people in countries with difficult contexts where you don't have to show the person's face. We've worked with wiki editorias to do illustrations instead, because all of that like art is also a part of visibilizing women. And now the time has read. So in case you're curious about how to get involved in the visible wiki women campaign, cover attend events, please get consent. We have a photo booth at wiki mania find us that our photo booth right outside and also will be on the fourth floor at the main hall later. So we have tools to support people to install photo booths which we think are feminist corners in big conferences, because big conferences can be overwhelming. So if you're looking for a corner to recharge and, and a feminist photo booth is a great space to be share your existing photos. So if you take pictures here, and you have those consents, please share those. Under the free license illustrate. If those, if you know illustrators, illustrators, if you work with graphics and you're able to illustrate different women, different non binary peoples realities and upload and then promote. So of course we are going to ask for promotion. This is free. But, but, but spread that word. Visible wiki women, women of colors, but the plurality is a theme for the year. And in September, in celebration of all the amazing work of women in sports, we thought to do this during World Cup, but we were too ambitious. We'll be unpacking body plurality in sports. So we are having a visible wiki women photo and at the contest will have flyers throughout wiki mania. And you're welcome to join us to also watch the FIFA women's World Cup final this Saturday. So yeah, so come to our photo booth. We are round. Um, guess you can tell I'm wearing very bright orange. You can't miss me today. And, um, Marianne and I and everybody at whose knowledge is really glad we're able to be part of the wiki women's summit. So thank you. Thank you. Thank you so much for bringing sunshine into the room.