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Starts 3 June 2026 23:16

Ends 3 June 2026

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Sustainability in the digital age: Environmental Impacts of AI Systems

Explore AI's environmental impact through carbon footprint analysis, energy accounting methods, and sustainable development practices for responsible technology deployment.
via openHPI

13 Courses


2 weeks

Optional upgrade avallable

Intermediate

Progress at your own speed

Free Online Course

Optional upgrade avallable

Overview

Welcome to the "Sustainability in the Digital Age" series Artificial Intelligence (AI) offers transformative potential across industries, but its development and deployment come with environmental costs. The course covers topics such as the carbon footprint of AI models, methods for measuring and reporting environmental impacts, and challenges in estimating the sustainability of AI technologies.

Students will gain insights into energy and carbon accounting, along with case studies demonstrating how AI’s environmental footprint is assessed. The course aims to provide a comprehensive understanding of the relationship between AI and the environment, equipping learners with knowledge to contribute to more sustainable AI practices.

This course is part of the Sustainability in the Digital Age series, a collaborative project between colleagues from Stanford University, SAP and the Hasso Plattner Institute.

Syllabus

  • Introduction to AI and Sustainability
  • Overview of AI technologies and their transformative potential
    Understanding sustainability in the context of AI
  • Environmental Impacts of AI Systems
  • The carbon footprint of AI models
    Energy consumption in AI training and inference
    Lifecycle analysis of AI systems
  • Measuring and Reporting Environmental Impacts
  • Methods for energy and carbon accounting
    Tools and frameworks for environmental impact assessment
    Reporting and transparency in AI's environmental impact
  • Challenges in Estimating AI Sustainability
  • Assessing direct and indirect impacts
    Barriers to accurate measurement and reporting
    Ethical considerations and trade-offs
  • Case Studies
  • Real-world examples of AI’s environmental footprint
    Successful case studies in reducing AI's environmental impact
    Lessons learned from industry and academia
  • Strategies for Sustainable AI
  • Designing energy-efficient AI algorithms
    Optimizing data centers and cloud services
    Policy and regulation for sustainable AI development
  • Future Directions and Innovations
  • Emerging technologies for sustainable AI
    The role of AI in promoting sustainability across sectors
    Collaborative efforts for a sustainable digital future
  • Conclusion and Course Wrap-Up
  • Recap of key insights and learning outcomes
    Opportunities for students to contribute to sustainable AI practices
    Next steps and resources for continued learning
  • Final Project and Assessment
  • Develop an environmental impact assessment for an AI technology
    Propose solutions to mitigate the environmental impact of AI systems

Taught by

Vanessa Parli


Subjects

Social Sciences