AI has the potential to transform industries, drive innovation, and improve decision-making. However, the increasing use of AI has raised concerns about its sustainability.

In essence, sustainability means that organisations engage in business that meets the needs of the present generation without compromising the ability of future generations to meet their own needs.

However, according to some estimates, AI systems could be responsible for up to 10% of global electricity consumption by 2025. This is a significant amount of energy. So, it’s vital for you to design, develop, deploy, and use AI sustainably.

In this post, we’ll explore five strategies for achieving sustainable AI.

1. Use renewable energy sources

AI requires a significant amount of energy to process data and train algorithms. As a result, it is essential for you to use renewable energy sources, such as solar or wind power, to reduce your carbon footprint.

AI is primarily deployed in the cloud. And cloud-service providers are increasingly using renewable energy sources to power their data centres. So, choosing a cloud provider that uses renewable energy sources can significantly reduce the environmental impact of AI.

2. Consider the entire lifecycle of AI systems

It’s crucial to consider the entire AI lifecycle, from development to decommissioning.

AI models require continuous training and updating, generating a significant amount of data and consuming energy. You can reduce their environmental impact by using efficient algorithms, compressing data, and recycling old hardware. Practically, you could do so by:

  • researching the most efficient means for sourcing and managing data;
  • requiring your third-party developers to make sustainability part of their development process; and
  • engaging with hardware providers that have a programme for recycling unusable hardware.

3. Prioritise ethical AI

Sustainability is not just about the environment but also about social responsibility. It means prioritising ethical AI to ensure your AI systems are fair, transparent, and unbiased.

In fact, it’s not just an ethical consideration. Data protection laws often require fairness, transparency, explainability, and accuracy in developing AI systems

By taking this approach, you can avoid negative social and environmental impacts that may arise from AI systems.

4. Develop beneficial AI

Sustainable AI requires you to develop AI systems that benefit society.

You can use AI to address some of the world’s most significant environmental and social challenges. For example, you could use AI to optimise energy usage, reduce waste, and improve sustainability in supply chains. So, it’s worth checking your AI projects to determine if you could develop AI systems that not only benefit your bottom line but also benefit society and the environment.

5. Cultivate collaboration

Collaboration is essential to achieving sustainable AI. Ideally, it would be best if you collaborated with other organisations, research institutions, and governments to share best practices and develop new solutions. By working together, you can contribute to AI that will benefit current and future generations.

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