{"signature": "0xac33d08d6098e192215c9cd015ca9d384911ad1ccac8b79bec3aa11293c307eb292cac48dde14ca804930fbcfe2d5a64535abf96ead2633927ff700ecb94da591b", "application": {"round": "0x09b14fe9786afdfcbb8ad8512715e5ecb6443db4", "answers": [{"type": "email", "hidden": true, "question": "Email Address", "questionId": 0, "encryptedAnswer": {"ciphertext": "N+K00L2wIQyuWQdmY/wZtMu9X3hM6qknJtyPwz4Y6Ggu88M+lLm31OpWa85e539e", "encryptedSymmetricKey": "fc51174f79b4c374e440655ecbaf7639c68a62b33a71c6778adc24ff3ed25fc66cc5080cf443aa6859aa95cc271f6513cca4c2148e1168edc0f59b4dc1b1c14ae23cbafb3e9c0601841f50ec47bc40906f5c50853448f0dedccaabdf492fa685fa33517369a858032cccef7cf903c7d40c8edf53a8acc296b49bfbceb1b6f3910000000000000020d472c288ee9beb03affa5d9b626280cb3fdf9a80b6dbe6d083d07377f0d58d0a67dff1778bfa091512cb97d179fa6b99"}}, {"type": "short-answer", "answer": "Gitcoin - through WomxninWeb3.TE", "hidden": false, "question": "Funding Sources", "questionId": 1}, {"type": "paragraph", "answer": "Madhuri Rahman - Product Manager - from UK and Bangladesh\nhttps://www.linkedin.com/in/madhuri-rahman \n\nAlex Zaremba - Machine Learning, LLM Prompt Engineering - Polish\nhttps://www.linkedin.com/in/zarembaalex\n\nAkhil Nedunuri - Frontend Developer from India\nhttps://www.linkedin.com/in/akhil-nedunuri-989a0aa8/\n\nAkinpelu Adedamola - Backend Developer - from Nigeria\nhttps://www.linkedin.com/in/akinpelu-adedamola-8794721a6/", "hidden": false, "question": "Please provide a list of your major team members, including their relevant experiences and social network/professional links. Please indicate if there any Asian builders.", "questionId": 2}, {"type": "short-answer", "answer": "@M_adhuri", "hidden": true, "question": "Telegram handle of major point of contact", "questionId": 3}, {"type": "paragraph", "answer": "We currently have our tool at MVP stage, where the LLM classifies hate speech text and assigns LE points. Here is a demo: \n\nhttps://www.loom.com/share/289eeeb9c19f4b6d9115f3005d8d87e8?sid=e7cbe3c0-2eea-44fb-91ca-5a1d890d15cf\n\nOur team work part-time on this project and so with a budget of $10,000 we can build a fully functional tool and begin to prototype with online gaming communities. \n\n- Complete the development and testing of the voting mechanism (12 weeks).\n- Improve the UI of the tool and add in user profiles (8 weeks). \n- Test a fully functional prototype with target user groups and gaming clients (12 weeks)", "hidden": true, "question": "Please demonstrate your financial plan here if it's not included in your project description.", "questionId": 4}, {"type": "paragraph", "answer": "This project will help create safer online gaming spaces in Asia (see the list of the top five games played in Asia below). These online spaces are public goods. Everyone is entitled to take part but they are not designed with everyone in mind. Our project will advance the way we govern these spaces in a way that works for everyone.\n \n1. PUBG Mobile (PlayerUnknown's Battlegrounds Mobile)\n2. Genshin Impact\n3. Free Fire\n4. Arena of Valor (Honor of Kings)\n5. Call of Duty", "hidden": true, "question": "Please demonstrate how your project would advance Asian public goods ecosystem.", "questionId": 5}], "project": {"id": "0xe7c3a7593e57260e68fe71ccc6931f37a4a83a0f9de95e79f82a32452d175adb", "title": "We.Lived.It - ", "logoImg": "bafkreicto5n6454t25oktfmavujojsyhuu2eybhajlt5oikhsa6cbtqld4", "metaPtr": {"pointer": "bafkreibbgiwsr4gfeoidz3t27lhib5h3hya2zipsmc5qcruk2qje5cjt2y", "protocol": "undefined"}, "website": "https://hate-speech-reporting-nextjs.vercel.app/", "bannerImg": "bafkreib54u53frd6mrwq4yfg2juxyhhvvvi7afty3alp7nuhye7tu547ne", "createdAt": 1723538843802, "userGithub": "TheBarnakhil", "credentials": {}, "description": "This is an experimental tool in decentralised AI and web 3. We are using the context of online gaming but plan to scale this experiment to other online communities.  \n\n**What's the context?**\n\nThe impact of gaming on mental health is not commonly talked about. Particularly during the pandemic, there was a spike in the number of players turning to gaming to avoid social isolation, with 2.7 billion players gaming in 2020. However, these spaces can also be fertile for toxic behaviour. We.Lived.It is a tool for people with lived experience of toxicity or marginalisation to reclaim these spaces.\n\n**Who is this for?**\n\nA general rule of thumb is that the more marginalised groups you belong to, the more hate speech you receive. In the current increasingly polarised society, people belonging to very different groups in terms of privilege and marginalisation often move through life in isolation from each other, not knowing much about each other\u2019s realities. \n\nAt the same time, marginalised groups have less access to technology and less chance to shape technological solutions. For technology to properly serve people from various communities, it needs to be able to reflect the lived experience of people from those communities. This applies to AI and tools for hate speech recognition.\n\n**What does the MVP do?**\n\nPlayers or users can use this tool to report their experiences of online hate speech through an LLM. When the LLM classifies it as hate speech, the person completing the report earns a \u2018lived experience point\u2019. These points earn the reporter decision-making power. Those with higher lived experience points have a greater weight in voting decisions around fine-tuning the LLM.\n\n**What do we mean by Fine-tuning the LLM?**\n\nIf the model produces a classification that the player disagrees with, they can trigger a vote for the community to fine-tune the model. \n\nFor example imagine the following text was returned as \u2018NOT HATE SPEECH\u2019, \u201c***You'd be cute if you lost some weight.\u201d*** . Perhaps in this community, Fatphobia is something that they felt their players or contributors should be protected from. The individual could trigger a community vote to add \u2018Fatphobia\u2019 to the model as a hate speech category. \n\n**What's the significance of lived experience points?**\n\nPlayers or users with greater lived experience points have greater weight in the vote to fine-tune the model. This is so that those directly impacted by hate speech are given decision-making power to govern the space and make it safer. \n\n**What Tech Are We Using**\n\n**LLM - GPT 3.5**\n\nThe starting point is GPT 3.5 turbo, a popular LLM which has been a common choice for hate speech recognition research.\n\n**Blockchain Network - Galadriel**\n\nGaladriel is an L1 that enables developers to build decentralized AI apps natively with Solidity - like ETH for AI.\n\n**Off-chain Voting Mechanism - Snapshot**\n\nSnapshot is an off-chain voting platform that allows DAOs, DeFi protocols, or NFT communities to participate in the decentralized governance easily and ...\n\n**What are the benefits of this tool?**\n\nPlayers are empowered to govern their spaces proactively as well as reactively by: \n\n- using tech to communicate with gaming companies to improve the inclusivity of their designs and avoid things like gaming bans in different countries based on inappropriate content: https://www.arabnews.com/node/1522256/%7B%7B\n\n- creating guidelines for how to interact with their communities online to share with the gaming developers.\n\n- submitting trigger warning requests to gaming companies on behalf of communities. Trigger warnings were developed by people from marginalised communities who often blogged about distressing content. The tool could be used to classify in-game content as \u2018distressing for X communities or people who\u2019ve experienced X trauma\u2019. These classifications could then be submitted to gaming companies to add trigger warnings.", "lastUpdated": 0, "projectTwitter": "womxninweb3_TE"}, "recipient": "0xa1F01E5cC9562ed061B0e3ddDd3e82Ef69A1CebD"}}