{"title": "We.Lived.It - ", "logoImg": "bafkreicto5n6454t25oktfmavujojsyhuu2eybhajlt5oikhsa6cbtqld4", "website": "https://hate-speech-reporting-nextjs.vercel.app/", "bannerImg": "bafkreib54u53frd6mrwq4yfg2juxyhhvvvi7afty3alp7nuhye7tu547ne", "createdAt": 1723538843802, "userGithub": "TheBarnakhil", "credentials": {}, "description": "This is an experimental tool in decentralised AI. We are using the context of online gaming but plan to scale this experiment to other online communities.  \n\nPlayers can use this tool to report their experiences of hate speech. When the AI agent classifies it as hate speech, the person completing the report earns a ‘lived experience point’. These points incentivise players to report hate speech because they earn decision-making power. They earn greater weight in voting decisions around fine-tuning the model.\n\nFor example, if the model produces a classification that the player doesn’t agree with, they can trigger a vote for the community to fine-tune the model. For example imagine the following text was returned as ‘NOT HATE SPEECH’, “***You'd be cute if you lost some weight.”*** . The individual could trigger a community vote to add ‘Fatphobia’ to the model as a hate speech category. Those players with greater lived experience points would have greater weight in the ensuing vote. This is so that those directly impacted by hate speech are given decision-making power to govern the space and make it safer. \n\nThis tool can be used to really tune into the nuances in experiences that people have in an online space. For example, the online space they are may have a large number of people from Polish backgrounds. In this scenario, they may want to fine-tune the AI model to recognise Polish slurs or Polish slang. The AI agent will be better at understanding the nuances of the online community space. \n\nThe AI agent could be used:\n\n- by players in games to access historical hate speech records to identify if there are repeat offenders and petition to have those people removed from online spaces.\n- by players to create more nuanced guidelines for how to interact with certain communities. For example, in case instances arise when people are enacting microaggressions unknowingly - e.g. around the use of gender pronouns.\n\nUltimately, players can decide on other ways to fine-tune the AI to serve their online spaces better.  For example, they could even use it to submit 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 ‘distressing for X communities or people who’ve experienced X trauma’. These classifications could then be submitted to gaming companies to add trigger warnings.\n\nThe LLM model\n\nThe starting point is GPT 3.5 turbo, a popular LLM which has been a common choice for hate speech recognition research. Already out of the box it can be used with some success for classification (also called: zero-shot learning) of hate speech, but it will not work well for all of the contexts. But there’s some interesting research that claims that a considerable improvement can be obtained with prompt engineering, comparable to adding new training data. Focusing attention on the prompt makes it easier to see how the voting and governance process can enter the model choice.\n", "logoImgData": {}, "bannerImgData": {}, "projectTwitter": "womxninweb3_TE"}