This toolkit is intended to provide you a framework that addresses privacy and governance at both the organizational and system levels. It provides suggestions for determining the right stakeholders to engage

The amazing potential and opportunity of Generative AI has also brought ethical concerns. How can you address these concerns effectively as you deploy tools and products powered by generative AI? In this course, expert Vilas Dhar equips designers and decision makers to better understand and act on the emerging ethical risks and considerations of generative artificial intelligence. Get a solid introduction to the ethical concerns around generative AI, then learn how to use a framework of ethical analysis to address these concerns. Plus, explore ways you can prepare your organization to address ethics in AI across all stakeholders from technology to C-Suite leaders.

This learning path begins with an introduction to UN 2.0, followed by an exploration of the five cross-cutting competence areas—known as the Quintet of Change. You'll start by building a solid foundation with LinkedIn courses that showcase how these competencies are applied in today’s business world. Next, dive deeper into each area with advanced sub-learning paths. These include high-level LinkedIn courses from which you can understand how you can build that cross-cutting competence into your work area. This is accompanied by key UN materials, essential policies, strategies, and tools. Finally, the journey concludes with a curated collection of UNICEF’s most valuable learning resources.

By taking this course, you'll learn to: - Deeply understand generative AI, describing the key steps in a typical LLM-based generative AI lifecycle, from data gathering and model selection, to performance evaluation and deployment - Describe in detail the transformer architecture that powers LLMs, how they’re trained, and how fine-tuning enables LLMs to be adapted to a variety of specific use cases - Use empirical scaling laws to optimize the model's objective function across dataset size, compute budget, and inference requirements - Apply state-of-the art training, tuning, inference, tools, and deployment methods to maximize the performance of models within the specific constraints of your project - Discuss the challenges and opportunities that generative AI creates for businesses after hearing stories from industry researchers and practitioners