Even in climate and weather science — domains often considered objective — bias and inequity can persist. From radar networks that miss rural regions to algorithms that confuse correlation for causation, uncritical AI risks reproducing social harms. The authors propose a framework for trustworthy environmental AI: diversify training data; audit models for fairness; involve local and Indigenous communities in data collection and decision‑making; and report uncertainty transparently. They also note the importance of “Green AI,” minimizing energy use so solutions don’t worsen emissions. Done right, ethical AI can enhance environmental justice, empower communities, and rebuild public trust in science. Produced by Cognivault — insight, intelligence, and innovation made clear.