Definitions

Bias mitigation refers to the systematic identification and reduction of unfair prejudice in algorithmic systems, particularly those using artificial intelligence and machine learning. It involves techniques and practices designed to prevent, detect, and address algorithmic biases that could lead to discriminatory outcomes against individuals or groups based on protected characteristics.

NIST AI Risk Management Framework


The process of implementing strategies or techniques to reduce the presence and impact of unwanted bias in machine learning and AI systems. Bias mitigation aims to ensure that algorithmic decisions do not discriminate against individuals or groups based on sensitive attributes such as race, gender, age, or disability.

IEEE 7003-2024 Standard for Algorithmic Bias Considerations


A set of processes, tools, and techniques for identifying, measuring, and reducing harmful bias in AI systems. Bias mitigation strategies can be applied at various stages of the AI lifecycle, including data collection, algorithm design, model training, evaluation, deployment, and monitoring.

AI Fairness 360 Toolkit, IBM Research

Key Aspects of Bias Mitigation

Bias mitigation encompasses several important dimensions:

  1. Pre-processing techniques: Methods applied to training data before model development to reduce inherent biases
  2. In-processing techniques: Modifications to learning algorithms during model training to ensure fairness
  3. Post-processing techniques: Adjustments to model outputs after training to correct for biased predictions
  4. Continuous monitoring: Ongoing evaluation of deployed systems to detect emergent biases
  5. Diverse representation: Ensuring diversity in data collection, algorithm design teams, and testing populations

Relationship to Other Qualities

Bias mitigation is closely connected to several other system qualities:

  • It is essential for fairness as it directly addresses discriminatory outcomes
  • It supports transparency by making algorithmic decision-making more understandable
  • It enhances reliability by ensuring consistent performance across different user groups
  • It contributes to safety by preventing harmful impacts on vulnerable populations
  • It improves accountability by establishing responsibility for algorithmic outcomes