As research and adoption of artificial intelligence continue to advance at an accelerating pace, so do the risks associated with using AI. To help organizations navigate this complex landscape, researchers from MIT and other institutions have released the AI Risk Repository, a comprehensive database of hundreds of documented risks posed by AI systems. The repository aims to help decision-makers in government, research and industry in assessing the evolving risks of AI. While numerous organizations and researchers have recognized the importance of addressing AI risks, efforts to document and classify these risks have been largely uncoordinated, leading to a fragmented landscape of conflicting classification systems. “We started our project aiming to understand how organizations are responding to the risks from AI,” Peter Slattery, incoming postdoc at MIT FutureTech and project lead, told VentureBeat. “We wanted a fully comprehensive overview of AI risks to use as a checklist, but when we looked at the literature, we found that existing risk classifications were like pieces of a jigsaw puzzle: individually interesting and useful, but incomplete.” The AI Risk Repository tackles this challenge by consolidating information from 43 existing taxonomies, including peer-reviewed articles, preprints, conference papers and reports. This meticulous curation process has resulted in a database of more than 700 unique risks. The repository uses a two-dimensional classification system. First, risks are categorized based on their causes, taking into account the entity responsible (human or AI), the intent (intentional or unintentional), and the timing of the risk (pre-deployment or post-deployment). This causal taxonomy helps to understand the circumstances and mechanisms by which AI risks can arise. Second, risks are classified into seven distinct domains, including discrimination and toxicity, privacy and security, misinformation and malicious actors and misuse. The AI Risk Repository is designed to be a living database. It is publicly accessible and organizations can download it for their own use. The research team plans to regularly update the database with new risks, research findings, and emerging trends.

Full report :  MIT releases a database of 700+ unique AI risks, based on 43 existing taxonomies, including peer-reviewed articles, preprints, conference papers, and reports.