Redscribe: protecting participant voices through private AI transcription

This study examines the deployment of Redscribe, an offline, privacy-first AI transcription tool designed to protect qualitative research participants' voices while maintaining strict data confidentiality. In an era dominated by cloud-based speech-to-text models that expose highly sensitive, identifiable narratives to external servers, this paper analyzes the structural advantages of executing end-to-end local transcription and speaker diarization. By confining all audio processing, transcript generations, and algorithmic operations strictly to local hardware, Redscribe establishes a robust technical framework that mitigates data leak risks and deductive disclosure. The findings suggest that utilizing device-level encrypted AI engines not only satisfies rigid institutional review board (IRB) requirements for human-subject research but also fosters participant trust, ultimately preserving the authenticity and integrity of marginalized, vulnerable, or legally sensitive voices in qualitative inquiry.