The need for trust in AI has been of importance and one way of achieving it is through mitigating discrimination and bias in machine learning models throughout the AI application lifecycle. This course will give you an overview on the concept of fairness which helps in building trust in AI and how "AI Fairness 360" open source toolkit can help you implement debiasing techniques to measure, understand and mitigate AI bias. Learners will be provided an overview of AI fairness and bias concepts, how to measure bias in models and how to apply fairness algorithms to reduce unwanted bias. It will also walk you through a demo of working of "AI Fairness 360" open source tool kit and using this tool kit on a real-world use-case.

Recognize the need of Trustworthy AI

Describe and differentiate various factors that can build trust in AI

Appraise situations that require a focus on fairness

Analyze where unwanted bias comes from

Recognize methods to mitigate unwanted bias

Many roles would find this course useful.

In order to be successful, you should have a basic understanding of data science, machine learning, and Python.

Recognize the need of Trustworthy AI

Describe and differentiate various factors that can build trust in AI

Appraise situations that require a focus on fairness

Analyze where unwanted bias comes from

Recognize methods to mitigate unwanted bias