What is Bias in Machine Learning?
Bias in machine learning refers to systematic errors that cause a model to produce unfair, inaccurate, or discriminatory results. It can originate from training data, model design, or the way results are interpreted and applied.
Why It Matters
AI systems increasingly make decisions that affect people's lives — hiring, lending, criminal justice, healthcare. When these systems contain bias, they can perpetuate or amplify existing societal inequalities at scale. Understanding and mitigating ML bias is critical for building AI that is fair, trustworthy, and beneficial.
How It Works
Bias enters ML systems at multiple stages:
Data bias (most common):
- Historical bias — training data reflects past discrimination (e.g., hiring data from a biased era)
- Representation bias — certain groups are underrepresented in training data
- Measurement bias — features are measured differently across groups
- Labeling bias — human annotators inject their own biases into labels
Algorithmic bias:
- Models amplify patterns in data, including discriminatory ones
- Optimization objectives may not account for fairness
- Proxies: even without explicit protected attributes, models find correlated features (zip code as proxy for race)