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As humanitarians at Data Friendly Space, we've seen firsthand how the landscape of humanitarian work has evolved, especially in the face of growing funding challenges. Now more than ever, the ability to quickly and accurately analyze crises is essential. With GANNET SituationHub, we aim to provide humanitarians with a powerful tech tool that blends artificial intelligence with human expertise to enhance and accelerate crisis analysis.
AI is an incredible asset when it comes to processing massive amounts of data in seconds. But as an organization deeply rooted in humanitarian analysis, we know that human insight is irreplaceable for understanding context, nuance, and cultural sensitivities. AI is just a tool. It’s the human expertise that ensures every analysis meets the highest standards of accuracy and relevance. We’ve designed a system where technology and human judgment work in harmony, reinforcing each other rather than replacing one another.
So, what does that actually look like in practice? The foundation of this approach lies in developing structured analytical frameworks that guide the SituationHub’s assessments. Every day, our analyst team refines these frameworks and works on improving their outputs, teaching the system how to interpret humanitarian crises in different contexts. When we deploy in a new country, we don’t simply apply a one-size-fits-all model. We carefully adapt our approach to account for local nuances, language differences, and specific regional challenges while ensuring the standardization and compatibility of these outputs.
At Data Friendly Space, we are committed to continuous analytical improvement. SituationHub goes beyond just describing events. It identifies patterns and interprets situations, incorporating lessons and feedback from our analysis team. We continuously work to make GANNET and SituationHub as good, reliable and accurate as our best analysts. We continuously refine bias prevention mechanisms and fact-checking protocols to ensure our conclusions are both accurate and fair. This makes a huge difference when humanitarian organizations rely on this information to plan their responses effectively.
Quality control is at the heart of our Human-In-The-Loop process. Every analysis generated by AI goes through rigorous human verification, where our team reviews everything from translation accuracy to source reliability. It’s a meticulous process, but it’s crucial for maintaining trust in the information we provide.
We also recognize that other organizations have different approaches to verification. That’s why GANNET SituationHub is designed for collaborative verification. While our analysts currently lead the quality control process, we’re working on ways to integrate feedback from the broader humanitarian community. This isn’t just about improving accuracy. It’s about building trust and making the analysis even stronger with collective expertise.
The world is changing, and as humanitarians, we must adapt. GANNET SituationHub represents a true partnership between human expertise and AI capabilities, striking the right balance between speed and accuracy, between comprehensive coverage and deep understanding. By maintaining this balance, Data Friendly Space ensures that humanitarian organizations keep human insight at the core of their work, delivering nuanced, context-aware, and reliably accurate analysis. Our goal isn’t just to make analysis faster or more cost-effective (though those are important benefits). It’s to make humanitarian response more effective where it matters most.
