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Realizing AI for Social Impact explores how AI can contribute to social impact, examining applications across various fields like public health and conservation. Learners will explore multiple AI techniques, from machine learning to reinforcement learning, while addressing the challenges of rapidly evolving technologies. The series emphasizes practical and ethical considerations of deploying AI, especially working with communities in resource-constrained environments.

Syllabus

Course 1: Defining AI for Social Impact
Course 2: Practical AI for Social Impact
Course 3: Participatory AI for Social Impact

Courses

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Artificial Intelligence (AI) is transforming how the world addresses some of its toughest challenges. In this course, "Defining AI for Social Impact", you’ll learn how AI is being applied in unique ways, from protecting endangered wildlife across the globe to expanding healthcare access in communities with limited resources. Through real-world case studies and faculty-led videos, you’ll gain a personal understanding of how AI can create positive social impact when applied thoughtfully and responsibly. You’ll explore both the potential and limitations of AI, learning to recognize the algorithmic, practical, and ethical considerations that ideally guide its use. This course goes beyond theory by connecting abstract ideas to concrete examples of existing AI projects in society. By the end, you’ll understand how AI works in practice and consider how to leverage it to create a more just and moral future. This is the first course in the three-course series, "Realizing AI for Social Impact", where you will explore use cases and frameworks for deploying AI to achieve social impact.
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AI is rapidly proliferating across society, but negative impacts can arise when communities and stakeholders are not equally involved in the AI development process. In this course, you’ll learn to critically evaluate AI's societal impact and apply participatory design methods, preparing you to develop ethical and inclusive AI solutions for the future. "Participatory AI for Social Impact" introduces the core philosophy of designing with rather than for users and other stakeholders. Motivated by analyzing case studies where AI for social impact did not achieve the desired goals in the real world, you will learn to identify participatory AI practices and principles and create your own participatory AI plan. We will also discuss the logistical and ethical nuances of implementing participatory AI for social impact, including working with communities. This course will provide a practical toolkit for developing ethical, inclusive AI solutions that prioritize collaboration and positive social impact. This is the third course in the three-course series, "Realizing AI for Social Impact," where you will explore use cases and frameworks for deploying AI to achieve social impact.
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Gain a foundational understanding of key AI algorithms and build valuable skills highly sought after in tech and social impact fields. "Practical AI for Social Impact" introduces you to algorithms, including machine learning and reinforcement learning, with additional topics and resources referenced throughout the course. Crucially, this course also addresses special challenges in implementing AI for social impact. Throughout the course, we’ll discuss how to navigate data scarcity, bias, and ethical deployment in real-world communities. Case studies will explore potential scenarios with sequential, adaptive decisions, helping you approach new situations with tact and knowledge. By the end of the course, you’ll have developed skills highly sought after in tech and nonprofit sectors, empowering you to optimize resource distribution and drive systemic change at scale. This is the second course in the three-course series, "Realizing AI for Social Impact", where you will explore use cases and frameworks for deploying AI to achieve social impact.