In an era where artificial intelligence influences hiring, healthcare, finance, and everyday decision-making, the demand for Responsible AI design has never been greater. This course empowers professionals, researchers, and innovators to design, evaluate, and communicate AI solutions that are transparent, fair, and trustworthy. Through practical frameworks and guided demos, learners will explore how to apply core Responsible AI principles-fairness, transparency, accountability, privacy, and safety-across the AI lifecycle. You’ll practice identifying bias and ethical risks, documenting safeguards using structured templates, and transforming complex technical work into clear, stakeholder-ready presentations. Real-world examples and corporate case studies demonstrate how leading organizations operationalize Responsible AI.
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Design & Present Responsible AI Solutions
This course is part of Build Next-Gen LLM Apps with LangChain & LangGraph Specialization
Instructors: Starweaver
Included with
Recommended experience
What you'll learn
Evaluate AI use cases by applying key Responsible AI principles such as fairness, transparency, and accountability.
Identify and document potential risks and biases across data, models, and user interactions using structured ethical design tools.
Develop and communicate stakeholder-ready presentations and documentation that clearly articulate Responsible AI design decisions.
Skills you'll gain
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December 2025
1 assignment
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There are 3 modules in this course
This module introduces learners to the foundational concepts of Responsible AI - exploring why ethical design, transparency, and accountability matter in modern AI systems. Learners will examine the core principles of Responsible AI, understand how bias and harm can emerge throughout the AI lifecycle, and discover how to embed ethical considerations into every stage of AI solution design. Through real-world case examples and structured reflection, this module establishes the mindset and vocabulary needed to design AI systems that are both innovative and trustworthy.
What's included
4 videos2 readings1 peer review
This module guides learners through the practical process of integrating Responsible AI principles into real-world system design. Learners will explore how to identify and mitigate ethical risks, detect and document bias, and evaluate model performance beyond accuracy metrics. They will learn to apply tools such as Responsible AI canvases, risk logs, and model cards to ensure transparency and accountability across the AI development lifecycle. By the end of the module, learners will be able to design AI systems that align with organizational values, regulatory standards, and human-centered goals.
What's included
3 videos1 reading1 peer review
This module focuses on transforming Responsible AI design work into clear, stakeholder-ready communication. Learners will discover how to structure and deliver presentations that effectively convey technical rigor, ethical awareness, and societal impact. The module covers techniques for visual storytelling, ethical reporting, and audience-tailored messaging to build trust and understanding among diverse stakeholders-executives, regulators, and the public alike. By the end of the module, learners will be able to craft compelling presentations and documentation that demonstrate both AI innovation and accountability.
What's included
4 videos1 reading1 assignment2 peer reviews
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