Responsible Innovation and Trustworthy AI

SAS connects trustworthy AI with the wider data and analytics lifecycle. Human-centered principles and bias review lead into scenarios about design, development and deployment. It is a foundations option for people making decisions with AI as well as those involved in creating the systems.

What you will learn

• Connect trustworthy AI with the data lifecycle.
• Identify unwanted bias in development and use.
• Apply responsible-innovation principles to scenarios.

Who this suits

Data users, managers and technical professionals; there are no formal prerequisites, although working data literacy is helpful.

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.

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Put it into practice

Review a fictional analytics proposal and explain how accountability, transparency and privacy would be considered at each lifecycle stage.

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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