Recommended for:
- Developers, engineers, and project managers involved in AI projects
- Policymakers and regulators shaping the future of AI governance
- Researchers and academics studying the societal impact of AI
- Anyone interested in building and deploying AI systems responsibly
You will:
- Gain a comprehensive understanding of the concept of AI sustainability.
- Explore the environmental and social impacts of AI systems.
- Learn about frameworks and best practices for sustainable AI development.
- Develop practical skills for integrating sustainability considerations into the AI workflow.
- Discover how to assess and mitigate the potential negative impacts of AI.
The AI Ethics and Governance in Practice series
Here is the list of titles available in AI Ethics and Governance in Practice series:
- AI Ethics and Governance in Practice: An Introduction
- AI Sustainability in Practice
- AI Fairness in Practice
- Responsible Data Stewardship in Practice
- AI Safety in Practice
- AI Explainability in Practice
- AI Accountability in Practice
Detailed Overview
AI Sustainability in Practice tackles the critical need to develop and deploy AI systems in a sustainable manner. This two-part series, developed by The Alan Turing Institute, goes beyond the technical aspects of AI to consider its broader societal and environmental implications.
Part One: Foundations for Sustainable AI Projects establishes the core principles of AI sustainability. It defines the concept as ensuring AI systems are “designed, developed, used and governed in a way that promotes long-term societal and environmental wellbeing.” This includes considerations such as:
- Environmental Sustainability: Minimizing the environmental footprint of AI systems through efficient resource utilization and energy consumption.
- Social Sustainability: Ensuring AI systems promote positive societal outcomes, foster inclusivity, and mitigate potential harms like bias and discrimination.
- Ethical Sustainability: Aligning AI development with ethical principles such as fairness, accountability, and transparency.
The first part of the series emphasizes that achieving AI sustainability requires a collaborative effort from various stakeholders, including developers, project managers, policymakers, and users. It introduces key frameworks and tools to guide this collaborative approach:
- Values-led Design: Emphasizes embedding ethical considerations and societal goals into the design process from the outset.
- Anticipatory Reflection: Encourages ongoing assessment of potential social and ethical impacts throughout the AI lifecycle.
- Collaboration and Stakeholder Engagement: Promotes active participation from diverse stakeholders in shaping responsible AI development.
Part Two: Sustainability Throughout the AI Workflow delves into the practical application of these foundational principles. It explores how sustainability considerations can be integrated throughout the various stages of AI development, including:
- Data Collection and Management: Focusing on responsible data sourcing, minimizing data collection, and ensuring data accuracy and fairness.
- Model Development and Training: Emphasizing energy-efficient training processes, reducing model bias, and using ethically sourced training data.
- Deployment and Monitoring: Prioritizing ongoing monitoring of AI systems’ impact, promoting explainability and transparency in decision-making, and establishing mechanisms for human oversight.
- End-of-Life Considerations: Planning for the responsible decommissioning of AI systems to minimize environmental impact and ethical concerns.
By providing these practical frameworks and tools, AI Sustainability in Practice empowers project teams to build AI systems that are not only effective but also contribute positively to a sustainable future. The series encourages a holistic approach to AI development, ensuring that this powerful technology serves humanity in a beneficial and responsible way.
Citation and Licensing
Leslie, D., Rincón, C., Briggs, M., Perini, A., Jayadeva, S., Borda, A., Bennett, SJ. Burr, C., Aitken, M., Katell, M., Fischer, Wong, J., and Kherroubi Garcia, I. (2023).
AI Sustainability in Practice. Part One: Foundations for Sustainable AI Projects. https://www.turing.ac.uk/news/publications/ai-ethics-and-governance-practice-ai-sustainability-practice-part-one-foundations
AI Sustainability in Practice Part Two: Sustainability Throughout the AI Workflow. https://www.turing.ac.uk/news/publications/ai-ethics-and-governance-practice-ai-sustainability-practice-part-two
These workbooks are published by The Alan Turing Institute and are publicly available on their website
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