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Algorithmic Bias and Fairness
Bias often persists through proxy features and historical patterns, even when sensitive fields are removed. Fairness requires explicit testing and sustained governance, not intent alone.
Lesson: measure fairness outcomes by group on a regular schedule.Why this matters in practice
Biased AI decisions can create legal exposure, customer harm, and trust loss. Fairness controls should be integrated into model evaluation, release gates, and post-deployment monitoring alongside core accuracy metrics.
What to check in your organization
1. Do pre-release tests include subgroup fairness and impact analysis?
2. Are proxy features identified and risk-assessed during model design?
3. Are fairness metrics monitored in production, not just in development?
4. Is there a remediation workflow when disparities exceed thresholds?
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