Artificial intelligence systems designed to monitor climate disasters frequently fail to interpret critical messages written in local dialects or slang, potentially jeopardizing safety during emergencies. While AI is increasingly used by governments and organizations to scan social media for environmental threats, these tools struggle with the way people actually communicate online. In regions like Nigeria, users often mix English with local expressions and slang, a linguistic phenomenon known as code-switching. For instance, phrases such as River don near our house o convey immediate danger, yet AI models trained on standard Western English often categorize such urgent pleas as casual commentary. The root of this issue lies in the training data for most AI systems, which is predominantly sourced from North America and Europe. Consequently, these models carry a cultural fingerprint that reflects Western norms and values, often diminishing or ignoring underrepresented voices from developing nations. This bias creates a significant barrier when analyzing climate journalism and disaster responses in the Global South, where local reporters are scarce and digital platforms are the primary source of information for the public. Unlike sarcasm detected in UK posts, subtle urgency cues in non-standard English varieties are frequently missed. This misinterpretation poses real risks. During floods, heat waves, or extreme weather events, failing to recognize the severity of a message based on its language could lead to a lack of timely intervention, endangering lives and property. Current AI systems rely on patterns found in past data, which means they perform well only when language conforms to expected standards. However, when posts contain local slang or unique cultural references, the algorithms often fail to grasp the context or emotion being conveyed. To address this, AI systems must be redesigned to reflect the diverse ways people communicate. Developers need to train models specifically on regional expressions and cultural contexts, moving beyond literal translations to understand nuanced meaning. Testing should be conducted on real-world online posts rather than formal English to ensure the technology can identify urgency and local references accurately. Furthermore, while automated systems can process vast amounts of data, human judgment must remain an integral part of the workflow, especially when public safety is involved. The goal is to create AI tools that can effectively assist communities in responding to climate emergencies. By improving the ability of these systems to interpret the nuances of everyday language, warnings and calls for help can be accurately identified and acted upon. This ensures that the full potential of AI in disaster response is realized, protecting vulnerable populations who rely on digital platforms for critical information during climate crises.