Transcribing speech is never neutral. It shapes power and bias

https://theconversation.com/transcribing-speech-is-never-neutral-it-shapes-power-and-bias-281527

Publish Date: 2026-05-06 16:19:00

Source Domain: theconversation.com

Here’s a polite and respectful summarisation of the key points from the article using an unordered list:

Language Technology Bias: The article elucidates how common language technologies often recognize and prioritize mainstream English standards over other dialects and indigenous names, such as “Boorloo” for Perth.
Autocorrect Example: Autocorrect transforming “Boorloo” into “Barolo” showcases how technology trained on prevalent English data fails to recognize and respect non-mainstream terms, reflecting the broader bias in language technologies.
Transcription Issues: It highlights the challenges faced in automatic speech recognition (ASR) when applied to non-standard dialects, including the misrepresentation of meaningful silences and pauses in Indigenous communication.
Impact of Transcription Errors: Recent research reveals that automatic and inaccurate subtitles can lead viewers to underestimate a speaker’s clarity and knowledge, influencing perceptions of both the message and the communicator.
Severe Consequences: For First Nations people in Australia, the misrecognition and errors in transcription can have severe real-world implications, affecting legal, medical, and welfare contexts.
Legal and Medical Risks: In critical sectors, such as healthcare, the use of AI transcription systems has resulted in significant mistakes, including the fabrication of diagnoses and inaccuracies that could lead to harmful outcomes.
Transnational Responsibility: The onus is on those using transcription technology to make their conventions clear, acknowledge limitations, and avoid ‘normalizing’ speech into a standard form that may not capture the richness of the original communication.
Call for Transparency: The article concludes by advocating for better, more diverse models for speech recognition and greater transparency on the part of those working with transcription tools, emphasizing the need to make visible and justify decisions about what is included and excluded in transcripts.