Machine learning models can unintentionally inherit biases from training data, leading to unfair outcomes. Here are key fairness techniques to mitigate such issues:
Data Preprocessing
- 🛠️ Rebalancing datasets to ensure equitable representation across sensitive attributes (e.g., gender, race).
- 🧾 Removing direct correlations between sensitive features and target variables.
- 📊 Visualizing data distributions to identify disparities.
Algorithmic Adjustments
- 🧠 Adversarial debiasing to train models that minimize bias against sensitive groups.
- 🔄 Reweighting samples to prioritize underrepresented classes.
- 🔍 Fairness-aware loss functions that penalize biased predictions.
Post-processing
- 📈 Calibration of model outputs to ensure fairness across groups.
- 📌 Threshold adjustment for different sensitive attributes.
- 🧪 Auditing predictions with fairness metrics (e.g., equality of opportunity).
For deeper insights, explore our introduction to fairness in ML. 🌐