 Yn ymdweud, mae'r amser Ysgrifennidol Ilywodol Ddechrau yn ymdweud y dyfodol yn ymdweud y gynhyrch a'r amser o'r cyd-fawr ynghylch gyda'r cyd-fawr. Mae'r cyd-fawr i'r cyfnodd, mae'r cyd-fawr yn ymdweud ymdweud. Mae'r cyd-fawr i'r cyd-fawr i'r cyd-fawr, mae'n gwybod i'r cyd-fawr i'n gwybod i'r cyd-fawr. So, Diane Abbott, number two of the Labour Party in Britain during the 2017 general election, Amnesty International did a study on all of the tweets that all MPs received. Of those classified as hateful, Diane Abbott received 45.1% of them. Laurie Penny, a journalist, writer, activist, often writing about feminist issues, by the age of 26, she received her first real bomb threat. And your Hannah Schmidt Nielsen, former leader of the Danish left-wing political party in Liston, alongside the usual barrage of threats against her body and harassment, it was once reported by a troll that she was in fact dead. Now, you might be sitting there thinking, okay, great Theresa, but men and male identifying people also suffer abuse online. And these women that you're describing, they are public personas, so maybe they should be expecting something like this. But really, the stats speak for themselves. It's not just public facing women that suffer this. Women are twice as likely to be sexually harassed online as men, mainly affecting our young women. 90% of all victims of revenge porn are female, and women are twice as likely to suffer adverse consequences as a result of this online abuse. Women and female identifying people disproportionately suffer online abuse, particularly those who challenge the status quo. Online sexism is real, and it's silencing the voices that society so desperately needs to hear. So at Opt Out, we're aiming to put a stop to the silencing nature of online sexism. We're building tools to help all female identifying people who've got something to say, get back to the online spaces that they've been chased out from. We're doing this by not only building tools, but by also building a movement. By holding workshops that give female identifying people a chance to come together and share their experiences. We're not only building the important, vital social infrastructure that these people need, but we're also allowing them to come together and, in doing so, act in a form of protest. By helping to form this community, we are able to spot needed technical infrastructure and make sure that the tools that we build are fit for purpose, ensuring that our tech is as community-driven as possible. In addition to our workshops, we aim to build a website that supports women getting their voices back. Inspired by Horasmap, an Egyptian-based NGO that you can see here, where an individual can submit reports of individual physical harassment, which then gets displayed to an online map, we will build our website to allow somebody to anonymously submit their experiences. This data will be stored, studied, and feed the models that our tools depend on. Our website will also transparently show details of what we're doing with the data and also the impact that our tools are having for women across the world, hoping to fuel the movement. Our long-term goal is to have a virtual Horasmap, which shows which communities on your chosen social media platform are sexist, sexually aggressive, or just downright nasty, enabling women and female-identified people to navigate the murky waters of online society as best they can. So, the opt-out ethos, the General Data Protection Regulation, or GDPR, has changed our lives on social media platforms. We have the right to be forgotten to dictate what is being recorded about us and to opt-out if we wish, but the abuse that women and female-identified people suffer online is not avoidable. We see opt-out as an extension of the GDPR that also protects the human rights of these people online, allowing them to join an online debate once more. So, what tools are we talking about? Alongside the website and the workshops, our main idea is a browser extension that filters out online sexism from an individual social media feed, and it does so by a sentiment, classification sentiment analyser. As you can see here, apologies, the video is not brilliant, you can't see the button, but you get the picture. So, currently our tool works on Twitter, and we've got a very, very simple neural net behind it that's trained on 10,000 of your normal troll tweets, but nothing sexism specific. Our plan going forward is to retune this model with a sexism-labeled dataset from Zirach Wazim and his co-workers, and once this is done and our website is up and running and the word has been spread, hopefully we will start generating a larger sexism dataset. But we're going to need to annotate this dataset, and we are proposing to do so with a two-round annotation scheme. Taking inspiration from Zirach once again and his co-workers, we're going to first label based on the categories generalised, directed, explicit and implicit. So, here are some examples of what that actually looks like in terms of language. Generalised, all students are lazy. Directed, you are a lazy student, which we may already have heard in our lives. Explicit, the candidate did not write enough papers, and implicit, the candidate was not an innovative researcher. But language is nuanced and complicated, and it can be combinations of all of these and also sexist. So, for example, the first comment there is both generalised using the Bridezilla word and then also directed because it's directed at somebody and similar sort of thing for the second. It's important, though, even though this is going to be a challenge, that we identify what is explicitly sexist first. Because if we are to encourage respectful debate and avoid creating any unintended echo chambers or biases with our tool, we need to get rid of the really obvious stuff first and then understand the implicit implied sexism later. So, once we've done this initial rounds of annotations, we're then going to further classify the comments based on five different labels taken from Maria and Savino's misogyny labels. What we have here underneath the different labels are tweet examples. So, discredit, slurring over women with no larger other intention. Stereotype and objectification to make women subordinate or description of a woman's physical appearance and slash or comparisons to narrow standards. And then dominance to preserve male control, protect male interests and to exclude women from conversation. Sexual harassment and threats of violence to physically assert power over women or to intimidate and silence women through threats and derailing to justify abuse, reject male responsibility and attempt to disrupt the conversation in order to refocus it. With this two-level annotation scheme, we will be able, we hope we will be able to identify the different faces of online sexism. So, in addition to this data annotation and understanding, we're going to deploy the what I call the three Cs approach. So, content, which is what I've already previously discussed, context and conversation. So, content will be using the sentiment analyzer with the labelings I just talked about. Context. So, who is the abuser in relation to the target? Are they part of a bigger mob attack? This is important to know. And then conversation. Has the sentiment of the conversation between the two taken a sustained nose dive? This could be an indication of intimate partner violence and requires a very different solution to what we're offering. With these labels and a better understanding of the behaviours and relationships of online sexism, we'll be better informed to answer the age or question of you know it when you see it, which is characteristic of online sexism. And so, once this is all done, we can start to build and test different models and really start to make a difference to women and female identifying people all over the globe. But what's the coolest thing, which I really, really like about our tool, is that we are consent focused, meaning that we aim to block what an individual finds distressing and not what we think. We're doing this by deploying a technique that I call big sister instead of big brother, where there will be a local instance of the model in somebody's browser that they can supply feedback to with the simple click of a button. The data stays locally, but people will be encouraged to share their labelings with opt out via the website. By focusing on individual consent and not a one model fits all approach, we ensure that the diverse range of online interactions are not stifled, but that productive and respectful interactions can flourish. Enabling female identifying people to join healthy debate is only possible if we also ensure that these people are safe online. We plan to do this by utilising the moderators that most social media platforms have effectively. Whenever our sentiment analyzes detective use, the comments will go automatically to the moderators with a traffic light labelling scheme, allowing them to prioritise more effectively what tweets or what comments, sorry, need attention immediately. This ensures that the user safety is never compromised. So once this is all said and done in the years down the line when we've got a great little NGO behind us, we're going to develop the browser extension. We're going to be able to have we're going to have a functionality that allows people to just use a blacklist of accounts. And so these people are automatically blocked from an individual's social media. These will be maintained and shared by what we call digilantes, which are groups of people that are seeded from the workshops that we're going to be holding that also act like a support network for anybody who has suffered online sexism. We then have the automatic replacement of comments, like I just described, and then finally a sentiment dashboard that pops up before the page loads with a traffic light labelling scheme for each comment, allowing the user to preemptively decide what they do and they don't want to see. So we've got a lot to do, as you can see, but we're planning to get working product by the end of August and then it'll be so popular we'll get a huge data set straight away and then we can start playing with that in September by the end of September and then what's really important is that we move across to different languages. We're going to design the web app so that all you need to do is change the data set and maybe some hyper parameters and you can change the language from English to Spanish to Romanian to whatever you'd like. This will enable us to build the community that we want behind it because online sexism is not restricted just to English. So with a topic like this I think it's really important for me to tell you all who's behind it. We're a bunch of volunteers at the moment apart from myself, well I'm working out of savings but most people are just working in their free time. We are a group of people from social scientists to data nerds but there is one characteristic that we all share. We won't let hate win. Our vision, we want to champion women back into the online worlds they've been chased out from, support them and their voices while still protecting them, unholding perpetrators accountable. We need to exist. If you share our vision, if you believe in the cause, I ask you to join us even if it's just by talking to somebody about the issue, about what I've discussed today, mentoring, code contributions, go on to the GitHub, Starros, all this stuff. I'm a relative novice. I've got about a year and a half worth of software engineering experience but the community has rallied behind me an incredible amount and this ship is sailing. So if you'd like to get on board just let me know. Online sexism has to stop Let's opt out. There's any questions? You can use the mics in the... Hope is not sexist to say like this first. Yeah, thank you for not being a sexist but this then is discriminating my height. Yeah, I think it's a really good idea. I'm really impressed by what you and your team is doing and I noticed one thing that I think is a really, really good idea which is like it's really customized to a certain user what he or she found that is offensive and then it's not one model fits all but that also raised a question in my mind is like that may be like technical challenges to kind of make it a customized model. Like it may require lots of resources and so have your team figured out like what's the approaches to overcome this challenge. I would really like to know if not then maybe we could find a solution to do it. No, so please let's talk about that. Yeah, thank you. Well, first of all, congratulations on your wonderful talk, wonderful explanation. Congratulations for the project itself. Thank you. Once sometimes it's too much easy to pretend that things doesn't exist or just happens to the others. However, I'd like to ask you more about the technical infrastructure that you developed but if you don't mind to clarify it a bit. Oh, you would like me to discuss. Yes, of course. So the browser extension is currently using Keras and TensorFlow and that's obviously for the NLP stuff but it's a very, very simple model. It's not even using any RNN or LTSM. It's very, very simple and the back end is just in a nice simple flask app. We all make mistakes. Should have been Django but there we go. It's fine but it's a very lightweight thing at the moment. What we're really focusing on is just trying to understand the science behind it first. So we're putting a lot of effort into research and getting different data sets and playing around with them. So the actual web infrastructure is a bit thin on the ground but if there are any front-end developers that would like to join please because I have no front-end experience so that would be really great and we don't have a front-ender at the moment. So any more questions? Feedback? Cool. You mentioned switching data sets to switch languages. Why do you need that? Why can't you put everything into one data set? Are there things that are sexist in one language and not in the other or is it too much data? I just presumed we'd need to do that. I just presumed we'd need to do that because I think sexism is so different in different languages than I think. That's an advantage because it's different. You can put all in the same pot and it won't disturb each other. That is a very, very good point. I don't know. I'm not a data scientist. Thank you for your talk. I actually have a question about business models, a non-technical question because I'm curious. I guess all of those social media is now allowed to flag offensive content. You say you want to develop a browser extension, but do you know how effective this offensive flagging is and it takes some time, I guess? Sorry, so are you saying that there's already something similar that the social media platforms are not right? It's another approach, right? Facebook, I guess you're targeting Twitter. It allows to flag offensive content. Yeah, but the individual still has to see it. By filtering it out, you don't see it at all. Exactly. Also, Twitter, Facebook, I have heard incidences when somebody has reported something and they've turned around and said, no, you're wrong. It's been very, very long for them to do something and to take down the comments. What this is trying to do is just, because if you are, for example, a politician and you socialise online, but you say something and then your feed is full of misogynistic or sexist abuse, it dilutes what you're really trying to do, which is just read the news or talk with friends. With this way of filtering it, the good stuff remains. All right. Thank you. Thank you for your talk. Are there plans to collaborate with social media platforms that, for example, if a person has a lot of tweets or comments that get flagged by a wide range of women that, for example, accounts could block or something like that? That would be great. This is a problem that a lot of the social media platforms are being pressured to solve, so it would be great if at some point we could be incorporated with the platforms, but we've not received an incredible amount of support. Let's put it like that. At some point, it would be great too, but we'll see. Thank you. I have a question. What kind of formats do you plan to support on the comments? In some social media platforms like Twitter, I suppose that a lot of the problems that people will experience also come in images or maybe audio or video. Do you plan or do you have some plans to tackle those or maybe in the pipeline at some point? At some point, yeah. At some point, but for now, just text. Going once, going twice. I'll ask that. Do you have many people using the extensions or like what's the right now or is it live or is it just like a daily? It's not live yet. Everything is in the testing, experimenting proof of concept. Yeah, exactly. We've got a proof of concept and we're hoping to take it to, there's a funding body called the Prototype Fund in Berlin in August. Mozilla is also based in Berlin, so we're only going to bring this out on Firefox to begin with because Chrome do have something similar, but if you, for example, if you hit their API, the perspective API with you are a feminist, it comes back as toxic. So their one size fit all approach is just not fitting anybody. So we're going to stick with Mozilla, maybe get some funding there, live in the dream, but yeah. So no, it's not live yet, but it will be soon. I've just struggled with Amazon Web Services for a week, so. So you just mentioned, you hope you might get funding from Mozilla? And you also said you're right now mostly operating it out of pocket basically. So do you have any other funding plans? Yes, so the Prototype Fund, which is, which fits us perfectly, so their categories are things like, you know, date, like internet health. And we already have some contacts within the Prototype Fund. So that's really the one that we're focusing on. Cool, I hope that works out. Thank you.