Responsible AI for Mental Health

Northeastern examines responsible AI through the particular challenges of mental-health applications. Use cases and technical foundations connect with privacy, bias, misinformation and social context. It is an educational review option for people assessing this domain; course completion does not establish clinical competence or validate a healthcare product.

What you will learn

• Compare mental-health AI use cases and limitations.
• Identify privacy, bias and safety concerns.
• Connect social context with responsible-system evaluation.

Who this suits

Learners interested in the ethical and technical review of mental-health AI, including technology and policy professionals.

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

Review a fictional mental-health chatbot proposal and document its intended boundaries, evidence gaps and the experts needed to evaluate it.

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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