Speech recognition technology has many applications, but bias can lead to poor performance for certain groups, such as non-native speakers, older adults, and people with disabilities. To mitigate bias, it is essential to use diverse training data and continually evaluate and enhance the system's performance on underrepresented groups. To diagnose bias, annotated data is needed, and metrics such as Character Error Rate (CER), Word Error Rate (WER), and Dialect Density Measure (DDM) can be used. Several datasets are available to analyze bias in ASR systems, such as the Speech Accent Archive, ACL Anthology, Santa Barbara Corpus of Spoken American English, Datatang's British English Speech Dataset, and the Artie Bias Corpus.