Algorithmic Bias
Algorithmic Bias (or AI Bias) occurs when a machine learning model generates systematic and repeatable errors that create unfair outcomes, typically due to prejudices or imbalances present in the training datasets.
Frequently Asked Questions
How does bias enter an AI model?▼
Primarily through the training data. If historical datasets contain human prejudices, underrepresent specific demographics, or contain correlation errors, the model will learn and replicate these biases.
How can developers mitigate algorithmic bias?▼
By auditing datasets for balance, using fairness metrics during evaluation, applying data augmentation to represent minorities, and training models with alignment constraints.
Quick Facts
- CategoryAlignment & Safety
- Key ApplicationHiring software screening audits, credit scoring validation, facial recognition tuning, and alignment checks.
Coverage Trend12 Weeks
Related AI Terms
Algorithmic Bias Media Coverage & Intelligence
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