Read/download the paper: cell.com/patterns/fulltext/S… Visit our website: airisk.mit.edu Preliminary mitigations taxonomy: airisk.mit.edu/ai-risk-mitig…

📢 New paper: Our write-up of the AI Risk Repository was recently accepted to Patterns at Cell Press. In 2024, we released the first version of the MIT AI Risk Repository, which synthesized over 700 risks from 44 frameworks into two taxonomies. We have just published an update on our database, which now synthesizes and categorizes over 1,725 AI risks from 74 frameworks. Since its release, our work has had a huge amount of impact, with more than 240,000 visits, >180 citations, >3000 inbound links from other websites, and support from Commonwealth Bank, the Society of Actuaries, and other collaborators. 📚Work in progress Since the release of the database, we have started to use our taxonomies in several related projects to make 'AI risk response gaps' more legible. 1️⃣ We have started to map AI incidents (in collaboration with the AI Incident Database) and ii) AI governance documents (in collaboration with the Center for Security and Emerging Technology (CSET). 2️⃣ We've identified and extracted over 2,000 mitigations for specific risks and created a corresponding AI risk mitigation taxonomy, which we hope to release in the upcoming months (I will link the preliminary version in the comments). 3️⃣ We have surveyed 272 experts to identify which risks they were most concerned about and which sectors or actors they thought were most vulnerable and responsible for addressing these risks.  4️⃣ We have surveyed thousands of public documents from large market cap organizations to identify which risks and mitigations they mention. This allows users to see coverage across sectors and actors at the document and excerpt level. We have a lot of other exciting (follow-up) work in the pipeline, but I'll save that for a future update. 🔗 How can you engage? Follow the MIT AI Risk Initiative, visit our website, explore the repository, read our preprint, offer feedback, or suggest missing resources or risks (see links in comments). 🙏 Please help us spread the word by sharing this with anyone relevant. We particularly appreciate connections with potential funders and financial supporters. We are significantly funding-constrained at the moment.  Thanks to everyone involved: Alexander Saeri, Jess Graham, Emily Grundy, Michael Noetel, Risto Uuk, Soroush J. Pour, James Dao🔸,Stephen Casper and Neil Thompson. cell.com/patterns/fulltext/S…

Great to see the MIT AI incident tracker, led by Simon Mylius, featured in @TIME: time.com/7346091/ai-harm-ris… The AI Incident Tracker maps incidents in the AI Incident Database according to the MIT AI Risk Repository’s causal and domain taxonomies, and assigns each incident a harm-severity score. Using an LLM, it processes raw incident reports, providing a scalable methodology that can be applied cost-effectively across much larger datasets as numbers of reported incidents grow. In the dashboard you can explore trends such as: - distribution of incident classifications by year - distribution of incident sub-domains by year - incidents with high direct harm severity scores by year - incidents causing severe harm in more than one harm category - distribution of harm severity scores by year Our last update added new evaluation fields for each incident, including: - 5 categories of NatSec impact: Physical Security & Critical Infrastructure / Information Warfare & Intelligence Security / Sovereignty & Government Functions / Economic & Technological Security / Societal Stability & Human Rights - A Fishbone/Ishikawa diagram that presents a number of potential causes for each incident - The primary goal of the AI system involved Visit our website to explore the data. Congratulations also to Daniel Atherton and the AI Incident Database (@IncidentsDB) for the coverage. We are lucky to be able to build on their critical work. Thanks to Harry Booth for the write-up (@HarryBooth59643)