What is bias mitigation in machine learning?

What is bias mitigation in machine learning?

Bias mitigation refers to techniques used to reduce bias in machine learning models, ensuring fairer outcomes.

Why is it important to address bias in AI?

Addressing bias is crucial to prevent discrimination and ensure that AI systems are equitable and trustworthy.

What are some common methods for bias mitigation?

Common methods include re-sampling, re-weighting, and using fairness constraints during model training.

Can you give an example of bias in AI?

Sure! An example is facial recognition systems that perform poorly on individuals from certain demographic groups.

How can I implement bias mitigation in my project?

You can start by analyzing your data for bias, applying mitigation techniques, and continuously monitoring model performance.