DATDAT-003 — Data Bias and Fairness Oversights
Voice Recognition Systems Force Non-Standard Speakers to Modify Behaviour
3/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
Algorithmic voice recognition systems perform unequally across speaker groups, imposing disproportionate adaptation burdens on those outside dominant linguistic norms. Organisations deploying such systems face equity liability and reputational risk if differential performance across user groups goes unaudited.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (Shelby2023) ↗https://airisk.mit.edu/
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