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Which risks should be considered when using AI systems, crafting rules, or regulating AI? It’s a complex question with no easy answers. From critical infrastructure safety to exam grading, each AI application brings its own set of risks.
The AI Risk Repository
In an effort to provide guidance to policymakers, stakeholders, and the AI industry, MIT researchers have created an AI “risk repository” – a comprehensive database of over 700 AI risks categorized by factors, domains, and subdomains. This repository aims to address the gaps and disagreements in AI safety research, offering a valuable resource for understanding and navigating AI risks.
Risk Assessment
The researchers collaborated with institutions and organizations to compile thousands of documents related to AI risk evaluations, identifying varying levels of risk recognition among different frameworks. While privacy and security concerns were commonly addressed, other risks like misinformation and discrimination were often overlooked.
Future Research
The MIT team plans to use the repository to evaluate how effectively different AI risks are being managed, aiming to highlight any deficiencies in organizational responses. By identifying and addressing overlooked risks, this research could lead to stronger and more comprehensive approaches to AI regulation and safety.
As the landscape of AI regulation continues to evolve, having a shared understanding of AI risks is crucial. While a database of risks is a step in the right direction, it will ultimately take collaborative efforts and ongoing research to ensure that AI technologies are developed and used responsibly.
