DATDAT-003 — Data Bias and Fairness Oversights
Algorithmic Bias and Opacity Identified as Dominant AI Ethics Failures
4/5Sector: OtherGeography: GlobalStage: DevelopIngested: —
Executive Summary
Systematic review finds over 20% of AI ethics literature centres on data bias, algorithmic unfairness, and opacity as persistent failure patterns. Boards lacking visibility into these risks face mounting regulatory exposure and reputational liability.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — What Ethics Can Say on Artificial Intelligence: Insights from a Systematic Literature Review (Giarmoleo2024) ↗https://airisk.mit.edu/
Could this happen in your organisation?
A Velinor AI Audit maps your active AI portfolio against the 50+ blindspots and benchmarks against documented sector failures like this one. A board-ready foresight document in 5 weeks.