TL;DR

AI bias occurs when systems produce unfair outcomes for certain groups. Detect it through testing, measure with metrics, and mitigate through data diversity, debiasing techniques, and ongoing monitoring.

Types of AI bias

Historical bias: Training data reflects past discrimination
Representation bias: Some groups underrepresented in data
Measurement bias: Labels or metrics favor certain outcomes
Aggregation bias: One model doesn't fit all subgroups
Evaluation bias: Testing doesn't cover all demographics

Real-world examples

  • Hiring AI rejecting female candidates
  • Facial recognition failing on darker skin tones
  • Credit scoring penalizing minorities
  • Healthcare AI missing symptoms in underrepresented groups
  • Search engines showing stereotypical images

Detecting bias

Test across demographics:

  • Gender, race, age, location
  • Compare accuracy and outcomes
  • Look for disparate impact

Audit training data:

  • Check representation
  • Identify skewed distributions
  • Review labeling consistency

Use fairness metrics:

  • Demographic parity
  • Equal opportunity
  • Equalized odds

Mitigation strategies

Data-level:

  • Collect more diverse data
  • Rebalance underrepresented groups
  • Remove sensitive attributes (with caution)

Algorithm-level:

  • Fairness-aware training
  • Adversarial debiasing
  • Constrained optimization

Post-processing:

  • Adjust predictions for fairness
  • Set different thresholds per group
  • Reweight outputs

Trade-offs

  • Fairness vs accuracy
  • Individual vs group fairness
  • Short-term vs long-term effects

Best practices

  1. Diverse development teams
  2. Regular bias audits
  3. Transparent documentation
  4. Stakeholder feedback
  5. Continuous monitoring

What's next

  • Responsible AI Deployment
  • AI Ethics Frameworks
  • Fairness Metrics Deep Dive