Harms to non-humans

Mitigation strategy

1. Implement rigorous systemic risk assessment and mitigation frameworks, including state-of-the-art model evaluations and enhanced cybersecurity protections for general-purpose AI models, to prevent unforeseen, large-scale negative externalities, particularly those related to issues of control and emergent capabilities such as artificial suffering. 2. Mandate and adhere to energy-efficient and sustainable practices across the AI lifecycle, such as prioritizing renewable-energy-powered data centers, simplifying model architectures, and leveraging transfer learning to minimize the significant carbon and water footprints that contribute to ecological and non-human harm. 3. Establish comprehensive and accessible audit trails and logging mechanisms throughout the AI system's design, development, and deployment phases, ensuring all model behaviors and human decisions are meticulously recorded to enable ex-post-facto analysis and facilitate the timely tracing and remediation of novel harmful outputs.