Bias Mitigation

Bias mitigation involves identifying and reducing unfair biases in AI systems and data that can lead to discriminatory outcomes. AI models learn from historical data, and if that data reflects existing biases, the AI will perpetuate them—unless actively corrected.

Where Bias Creeps In

• Training data: Historical data may underrepresent certain groups
• Feature selection: Using attributes that correlate with protected characteristics
• Model design: Algorithms optimizing for outcomes that favor certain groups
• Feedback loops: Biased predictions reinforcing biased outcomes

Mitigation Techniques

Audit training data for representation gaps, test models across demographic groups, use fairness-aware algorithms, monitor predictions for disparate impact, and establish human oversight for high-stakes decisions.