# Bias Mitigation Techniques > [!metadata]- Metadata > **Published:** [[2025-02-09|Feb 09, 2025]] > **Tags:** #🌐 #learning-in-public #artificial-intelligence #ethical-ai #bias-mitigation Techniques for addressing [[Algorithmic Bias]] in AI systems can be applied at different stages of the machine learning pipeline. These approaches aim to promote fairness and reduce discriminatory outcomes. ## Pre-Processing Techniques Techniques applied to training data before model development: 1. **Reweighting**: - Assigns different weights to training examples - Balances representation across groups - Compensates for historical biases 2. **Resampling**: - Over-sampling minority groups - Under-sampling majority groups - Creates balanced class distribution 3. **Disparate Impact Remover**: - Alters feature values - Reduces disparities between groups - Maintains predictive performance 4. **Fair Representation Learning**: - Uses [[Variational Fair Autoencoders]] - Creates bias-resistant data representations - Promotes fairness in downstream tasks ## In-Processing Techniques Techniques integrated into model training: 1. **[[Adversarial Debiasing]]**: - Uses adversarial learning - Removes sensitive information - Balances accuracy and fairness 2. **Regularization**: - Adds fairness terms to loss function - Penalizes biased outcomes - Guides model toward fair predictions 3. **Fairness Constraints**: - Imposes explicit fairness criteria - Ensures adherence to fairness metrics - Optimizes for both performance and fairness ## Post-Processing Techniques Techniques applied after model training: 1. **Threshold Adjustment**: - Modifies decision thresholds per group - Equalizes opportunity across groups - Fine-tunes model outputs 2. **Calibration**: - Ensures reliable probability predictions - Adjusts confidence scores - Improves fairness in probabilistic outputs 3. **Reject Option Classification**: - Allows model to abstain from decisions - Reduces high-risk unfair outcomes - Provides human oversight option ## Evaluation and Monitoring Continuous assessment through: - Regular audits - Fairness metrics tracking - Performance monitoring - Bias detection systems ## Implementation Considerations 1. **Context Specificity**: - Choose techniques based on use case - Consider domain requirements - Align with [[Fairness Definitions]] 2. **Trade-offs**: - Balance accuracy vs. fairness - Consider computational costs - Evaluate implementation complexity [Learn more about bias mitigation techniques and their effectiveness](@https://holisticai.readthedocs.io/en/latest/getting_started/bias/mitigation/inprocessing/bc_adversarial_debiasing_adversarial_debiasing.html)