A new public database lists all the ways AI could go wrong
The AI Risk Repository launched by MIT's CSAIL documents over 700 potential risks of advanced AI systems, emphasizing the need for ongoing monitoring and further research into under-explored risks.
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A new public database, the AI Risk Repository, has been launched by the FutureTech group at MIT's CSAIL to document over 700 potential risks associated with advanced AI systems. This comprehensive resource aims to enhance understanding of the dangers posed by AI, which can include bias, misinformation, addiction, and even the potential for creating biological or chemical weapons. The database reveals that most risks are identified only after AI models are deployed, with only 10% recognized beforehand. The creators emphasize the need for ongoing monitoring of AI systems post-launch, as many risks cannot be fully assessed prior to deployment. While the database is thorough, it does not rank risks by severity, which some experts believe could limit its practical utility. The initiative is intended to stimulate further research into under-explored risks and to encourage feedback for continuous improvement. The creators acknowledge that simply listing risks is not enough; translating these risks into actionable strategies is crucial for effective risk management in AI development.
- The AI Risk Repository documents over 700 potential risks of advanced AI systems.
- Most AI risks are identified post-deployment, highlighting the need for ongoing monitoring.
- The database does not rank risks, which may limit its practical application.
- The initiative aims to stimulate further research into under-explored AI risks.
- Continuous feedback and updates are encouraged to enhance the database's utility.
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