GLOSSARY -> Account Based Marketing - ABM

Bias Mitigation

Strategies and techniques to reduce or eliminate unfair biases in AI models, data, and decision-making processes to ensure equitable outcomes.

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.

LEARN