As the field of artificial intelligence continues to advance, the risks associated with its use also increase. To address these risks, researchers from MIT and other institutions have developed the AI Risk Repository. This database contains over 700 documented risks posed by AI systems, providing decision-makers in government, research, and industry with valuable insights on assessing the evolving risks of AI. The need for such a repository arose from the fragmented landscape of conflicting risk classification systems that currently exist.
The AI Risk Repository consolidates information from 43 existing taxonomies, including a variety of sources such as peer-reviewed articles, preprints, conference papers, and reports. By using a two-dimensional classification system, the risks are categorized based on their causes and classified into seven distinct domains. This approach helps in understanding the circumstances and mechanisms by which AI risks can arise, including issues related to discrimination, toxicity, privacy, security, misinformation, malicious actors, and misuse.
The AI Risk Repository is designed to be a living database that organizations can freely access and download for their own use. It serves as a practical resource for those developing or deploying AI systems, offering a checklist for risk assessment and mitigation. By utilizing the repository, organizations can identify specific risks associated with their AI applications and develop appropriate strategies to address them, such as mitigating discrimination and bias in AI-powered hiring systems or understanding the risks of AI-generated content in content moderation.
The research team behind the AI Risk Repository plans to regularly update the database with new risks, research findings, and emerging trends. They also intend to involve experts in reviewing the risks and identifying any omissions in the repository. This ongoing effort aims to provide organizations with the most up-to-date information on AI risks and help them tailor their risk assessment and mitigation strategies accordingly.
Beyond its practical implications for organizations, the AI Risk Repository also serves as a valuable resource for AI risk researchers. The structured framework provided by the database and taxonomies enables researchers to synthesize information, identify research gaps, and guide their investigations. This comprehensive database saves researchers time and increases oversight by offering a more in-depth understanding of AI risks.
As the field of artificial intelligence continues to evolve, the AI Risk Repository will play a crucial role in identifying potential gaps and imbalances in how risks are being addressed by organizations. By continuously updating the repository and engaging with experts in the field, the research team aims to provide valuable insights into the most significant risks and how they can be effectively mitigated. The repository will remain a useful resource for researchers, policymakers, and industry professionals working on AI risks and risk mitigation.