A company collecting global images of insect bites—covering different genders, ethnicities, and regions—for training a mobile diagnostic app is following which responsible AI principle?

Collecting diverse images across genders, ethnicities, and regions directly addresses the principle of Fairness. This ensures the AI model trained on these images performs equally well for all user groups, preventing bias where the app might accurately diagnose insect bites for one demographic but fail for another due to underrepresentation in the training data. Explainability focuses on understanding how an AI makes decisions, not the data it's trained on. Governance involves the policies and processes for AI development and deployment, which is broader than data collection. Transparency relates to openly communicating AI capabilities and limitations, not the inherent fairness of its performance across groups.