Georgianna Shea & Zachary Daher
Artificial intelligence (AI) is no longer a science fiction fantasy—but AI systems are only as good as the code, training data, and algorithms used to create them. As AI continues transforming industries, understanding and addressing its inherent risks is paramount. What’s needed now is a robust framework to manage AI vulnerabilities. Cybersecurity is light years ahead of AI. By applying lessons learned from cybersecurity, effective strategies can be developed to ensure the responsible and trustworthy advancement of AI technologies.
That AI systems are only as good as their inputs—and, consequently, can do great damage—is indisputable. Consider, for instance, a scenario where an AI system designed to monitor water quality inadvertently underreports contaminants due to flawed training data. This could lead to entire communities consuming unsafe water, resulting in public health crises and loss of trust in technology and government.
The recent introduction of MIT’s AI risk repository offers a promising tool for categorizing and analyzing these threats. The repository serves as an invaluable resource. Aggregating hundreds of AI-associated threats across various environments and categorizing these risks based on their causes and natures—whether related to privacy, security, disinformation, or other concerns—enhances the probability of risk mitigation.
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