"It feels like we're not meeting the criteria": Examining and Mitigating the Cascading Effects of Bias in Automatic Speech Recognition in Spoken Language Interfaces

Researchers have demonstrated that Automatic Speech Recognition (ASR) systems perform differently across demographic groups (i.e. show bias), yet their downstream impact on spoken language interfaces remains unexplored. We examined this question in the context of a real-world AI-powered interface that provides tutors with feedback on the quality of their discourse. We found that the Whisper ASR had lower accuracy for Black vs. white tutors, likely due to differences in acoustic patterns of speech. The downstream automated discourse classifiers of tutor talk were correspondingly less accurate for Black tutors when presented with ASR input. As a result, although Black tutors demonstrated higher-quality discourse on human transcripts, this trend was not evident on ASR transcripts. We experimented with methods to reduce ASR bias, finding that fine-tuning the ASR on Black speech reduced, but did not eliminate, ASR bias and its downstream effects. We discuss implications for AI-based spoken language interfaces aimed at providing unbiased assessments to improve performance outcomes.