Balancing AI's risks
This category constitutes more than 16% of the articles and focuses on addressing the potential risks associated with AI systems. Given the ubiquity of AI technologies, these articles explore the implications of AI risks across various contexts linked to design and unpredictability, military purposes, emergency procedures, and AI takeover.
ENTITY
3 - Other
INTENT
3 - Other
TIMING
3 - Other
Risk ID
mit579
Domain lineage
7. AI System Safety, Failures, & Limitations
7.3 > Lack of capability or robustness
Mitigation strategy
1. Implement comprehensive, structured AI Risk Management Frameworks (e.g., NIST AI RMF) to govern the AI lifecycle from design through deployment, ensuring the cultivation of a safety-centric organizational culture and establishing clear lines of accountability for ethical and compliance oversight. 2. Prioritize the technical robustness of AI systems by mandating rigorous adversarial testing (red teaming) and integrating defenses such as input validation and continuous vulnerability management to proactively mitigate threats stemming from model unpredictability and manipulation. 3. Establish Human-in-the-Loop (HITL) processes for critical, high-impact decisions, and utilize Explainable AI (XAI) methodologies to ensure transparency and interpretability of model outputs, thereby facilitating human review, challenge, and correction of potential errors or biases.