Safety – ABCDQI

AI in Public Sector: Google Public Sector collaborates with government agencies to enhance operations using AI and cloud computing across diverse domains such as transportation and environmental management.
Road Safety Initiative: The New York State Thruway Authority implemented an AI system that detects over-height trucks to prevent collisions with bridges, saving millions in potential damages.
Data-Driven Accident Prevention: Iowa researchers are leveraging historical accident data and connected car information to predict the probability of fatal crashes, improving police resource allocation.
Enhancing Scientific Productivity: The nonprofit Ai2 promotes transparency in AI models, enabling scientists to trust and effectively utilize AI to automate tedious research tasks and manage information overload.
Environmental Management Solutions: South Florida Water Management District uses Google Cloud to model water resource management, balancing between flood control and drinking water supply while facilitating scientific discovery.

Narrow Focus on Extinction Risk: The predominant framing of ‘AI safety’ as primarily addressing extinction risk limits the understanding and scope of safety concerns, detracting from everyday system vulnerabilities.
Diverse Safety Research: A systematic review of 383 peer-reviewed papers shows that the majority of AI safety research addresses practical challenges and cybersecurity rather than solely focusing on the extinction risk associated with superintelligent AI.
Adopting Historical Safety Principles: Drawing from established safety practices in aviation, pharmaceuticals, and cybersecurity can enhance AI safety governance by integrating redundancy, continuous monitoring, and adaptive governance processes.
Advantages of a Wider Safety Agenda: A pluralistic view of AI safety allows for targeted legislation on observable failure modes, eases coalition building among experts, improves oversight mechanisms, and aligns industry compliance with practical standards.
Concrete Lines of Action: Advancing AI safety requires codifying safeguards, institutionalizing incident reporting, funding safety science courts, and investing in open-source oversight tools, all while maintaining focus on both immediate risks and long-term existential threats.

Tag: Safety