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Algorithmic Bias
Systematic errors in AI outputs arising from skewed, imbalanced, or prejudiced training data.
Definition
Systematic errors in AI outputs arising from skewed, imbalanced, or prejudiced training data.
How It Works
Algorithmic Bias helps teams build predictable AI and translation workflows by setting clear expectations for quality, consistency, and decision-making.
In production environments, this concept is applied with process controls such as human review, terminology alignment, and repeatable quality checks across multilingual content.
Key Concepts
- core principle of algorithmic bias
- workflow-level implementation
- terminology and quality consistency
- human validation before publication
Where It Is Used
- localisation workflows
- AI translation pipelines
- multilingual content production
- cross-referencing related concepts such as Accountability in AI