In a research paper published on the arXiv preprint* server, researchers at the MIT AI Risk Repository addressed the fragmented understanding of artificial intelligence (AI) risks by creating an extensive AI risk repository comprising a living database of 777 risks categorized into two taxonomies. These taxonomies classified risks as high-level causal factors and specific domains such as discrimination, privacy, and system safety. This repository offered a publicly accessible and systematic approach to comprehensively defining and managing AI risks, enabling better coordination and practical response efforts.
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