Sustainable AI: Balancing Innovation with Responsibility
Learn more about sustainability in AI, which considers the needs of present deployments without compromising those of future generations. When it comes to technological advancement and societal impacts, are they compatible? They can be, but it requires thoughtful planning and collective action. Stakeholders must demand that AI systems are developed and deployed in ways that address long-term social, economic, and environmental considerations.
We introduce the three pillars of AI sustainability: social, economic, and environmental impacts. Explore the carbon footprint of AI systems, the ethical sourcing of data and resources, and the long-term implications of over-reliance on unsustainable AI models.
Self-Learning
Reflect on the lifecycle and sustainability of AI systems:
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What is the environmental impact of training and deploying an AI model? How does energy consumption scale with model complexity?
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What do you know about the overuse of computational resources? Download materials to explore the carbon footprint of AI.
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Where does the data for AI systems come from? Is it ethically sourced and representative of diverse communities?
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How can AI be designed to minimize environmental and social harm while maximizing benefits?
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Consider the lifecycle of an AI system: from data collection to training, deployment, and decommissioning. Who develops it? Who monitors its performance and ensures fairness? Do you need a new system for every task, or can existing models be optimized? By adopting sustainable AI practices, you contribute to preserving finite resources and fostering ethical innovation.
AI Sustainability Tracker
Monitor the resource usage and lifecycle impact of your AI systems. Use a tool to track energy consumption, identify optimization opportunities, and assess compliance with sustainability standards.