Responsible AI for Developers: Fairness & Bias

Google Cloud focuses on the practical identification and mitigation of bias in AI and machine-learning work. Fairness concepts connect with data and modeling choices and supporting tools. It is a technical continuation for learners who want to investigate how a system’s behavior can differ across people or situations.

What you will learn

• Explain fairness and bias in an ML context.
• Identify potential bias in data and modeling choices.
• Compare methods for evaluating and mitigating bias.

Who this suits

Intermediate learners exploring responsible-AI development; preview the technical exercises and recommended preparation before starting.

Before you enroll

Compare dated course examples with current provider documentation and the context of the proposed system. A framework is a starting point for review, not proof that a system is safe or compliant. Use synthetic examples and document assumptions, affected groups and unresolved questions.

Coursera offers paid enrollment or subscription access. Check the enrollment screen for assessment and certificate terms, trials and any financial-aid options. Provider access terms can change; review them before paying.

Put it into practice

Evaluate a permitted sample classifier across meaningful groups and document metric differences, data limitations and questions that remain unresolved.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at Coursera.

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