3 ways AI can improve disaster resilience and relief efforts

First, enhance collaboration between current initiatives, focused on specific use cases between a few partners, into a more impact-focused network of AI-driven disaster support. The attention currently devoted to developing algorithms should be balanced with at least as much energy and resources to make sure these tools are widely available and used on the front line of disaster relief. In many cases, that means more capability building. We also see duplication of efforts, with the data science community working on similar use cases, which could be streamlined. One option might be to establish a domain-specific partnership or coalition across which industry and global agencies would coordinate focused development teams, as just one model. Second, in the near term, develop more basic data capture and coordination tools across different agencies on the ground, rather than focusing the majority of investment on highly advanced AI.