Responsible and Ethical AI

Northeastern combines ethical questions with practical approaches to building responsible AI systems. Bias, privacy and risk considerations connect with trustworthy-development frameworks and hands-on labs. It is a technical continuation for learners able to use Python and understand basic model development.

What you will learn

• Evaluate bias and fairness considerations.
• Identify privacy and risk issues in AI systems.
• Apply responsible-system frameworks in practical labs.

Who this suits

Learners with Python knowledge and basic AI model-development skills, following the course’s stated lab requirements.

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

Apply a review framework to a fictional model and identify which fairness, privacy and risk claims can be supported by the available evidence.

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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