DATDAT-003 — Data Bias and Fairness Oversights
Voice Recognition Systems Fail Marginalised Users, Forcing Identity Compromise
3/5Sector: TechnologyGeography: GlobalStage: OperateIngested: —
Executive Summary
Algorithmic voice systems trained on non-representative data systematically underperform for marginalised users, compelling them to alter natural speech and identity to function. Organisations deploying such systems face material inclusion failures, regulatory exposure under equality frameworks, and reputational liability.
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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