What is a machine learning bias audit?

What is a machine learning bias audit?

A machine learning bias audit is a process to evaluate and identify biases in ML models, ensuring fairness.

Why is it important to conduct a bias audit?

Conducting a bias audit is crucial to prevent discrimination, ensure ethical AI use, and improve model accuracy.

How can I perform a bias audit on my model?

You can perform a bias audit by analyzing training data, testing for disparate impact, and using fairness metrics.

What tools are available for bias auditing?

There are several tools like AI Fairness 360, Fairlearn, and What-If Tool that can help with bias auditing.

Can you give me an example of bias in machine learning?

Sure! An example is a hiring algorithm that favors candidates from a specific demographic, leading to unfair outcomes.