  Useful conversational agents must accurately capture named entities to
minimize error for downstream tasks, for example, asking a voice assistant to
play a track from a certain artist, initiating navigation to a specific
location, or documenting a laboratory result for a patient. However, where
named entities such as ``Ukachukwu`` (Igbo), ``Lakicia`` (Swahili), or
``Ingabire`` (Rwandan) are spoken, automatic speech recognition (ASR) models'
performance degrades significantly, propagating errors to downstream systems.
We model this problem as a distribution shift and demonstrate that such model
bias can be mitigated through multilingual pre-training, intelligent data
augmentation strategies to increase the representation of African-named
entities, and fine-tuning multilingual ASR models on multiple African accents.
The resulting fine-tuned models show an 81.5\% relative WER improvement
compared with the baseline on samples with African-named entities.
