What is Sustainable AI? Understanding the Future of AI and Sustainability

‍

As AI's capabilities continue to expand, a pressing concern arises: how do we ensure that AI serves humanity without causing harm? This question highlights the need for sustainable AI, an essential framework that balances technological progress with environmental, ethical, and societal considerations while mitigating AI risk and the cost of non-compliance.

‍

But what exactly does sustainable AI mean? Why is it so crucial, and how can businesses ensure they adopt AI technologies in a way that is responsible and sustainable? In this article, we will explore these key questions and provide insights into creating a future where AI fosters innovation while also prioritising the greater good.

‍

What is Sustainable AI?

‍

Sustainable AI refers to the development and deployment of artificial intelligence in ways that are not only efficient and scalable but also responsible in terms of environmental, societal, and governance implications. It is about balancing the power of AI innovation with an obligation to minimise its environmental footprint and ensure that its outcomes benefit society as a whole. In the widest sense, implementing AI should not put organisations into conflict with the UN’s Sustainable Development Goals.

‍

The concept of sustainable AI builds on the foundation of ethical AI, which ensures fairness, transparency, and accountability, while incorporating a new dimension: ecological sustainability. Given AI's growing influence across industries and the resulting carbon footprint, ensuring it does not unduly contribute to environmental degradation is essential.

‍

The three dimensions of Sustainable AI:

‍

• Environmental sustainability: Reducing the energy consumption and carbon footprint of AI technologies.
• Social sustainability: Ensuring AI applications benefit society equitably and promote inclusivity.
• Ethical sustainability: Minimising risks related to bias, discrimination, and privacy violations.

‍

These principles are vital in ensuring that AI does not exacerbate existing problems or create new ones. If not addressed, the rapid expansion of AI could have significant negative consequences on both society and the planet.

‍

The Difference Between “Sustainable AI” and “AI for Sustainability”

‍

It is important to distinguish between Sustainable AI and AI for Sustainability:

‍

Sustainable AI refers to the practice of developing and deploying AI systems in ways that are energy-efficient, ethical, and environmentally friendly. This focuses on reducing the negative environmental impact of AI itself, while also promoting AI transparency. The fundamental question is: is it socially acceptable to operate the AI system in this way, and for this purpose?

‍

In contrast, AI for Sustainability involves using AI technologies to solve environmental challenges, such as reducing carbon emissions, conserving resources, and promoting green initiatives. The fundamental question here is, what can AI tools do to reduce the adverse impact of human activity on the environment?

‍

Both concepts are critical, but they address different aspects of the relationship between AI and sustainability.

‍

The Environmental Impact of AI

‍

AI systems, especially machine learning models, require significant computational power. Training large models often demands immense processing capabilities, consuming vast amounts of energy. For instance, research from the University of Massachusetts Amherst found that training a single AI model can emit more carbon than five cars over their entire lifespan. A recent study by Hugging Face showed that generating a single image consumes as much energy as fully charging a mobile phone.  Studies unanimously point to the fact that large AI systems consume large amounts of energy, both in training and production.

‍

These findings paint a stark picture of AI’s potential environmental cost.

‍

The primary drivers of this energy consumption are data centres, which house the infrastructure needed for running AI models. Data centres are estimated to consume about 1% of the world’s electricity, a number that is only expected to rise. Some studies argue the carbon footprint of the global cloud infrastructures that houses AI systems, amongst others, is equal to the carbon footprint of commercial aviation.

‍

With this in mind, developers and AI practitioners are increasingly tasked with finding ways to make AI energy-efficient, sustainable, and environmentally friendly.

‍

Strategies for Sustainable AI

‍

1. Energy-Efficient AI Models

‍

One approach is optimising AI models to use less energy. Researchers are already exploring ways to create leaner models, requiring fewer computations and less data. Transfer learning, for example, allows models to apply knowledge gained from one task to another, reducing the need for fresh training from scratch. Similarly, techniques like quantization and pruning can shrink models without sacrificing performance, which directly translates to lower energy use.

‍

It is a widely known fact in the machin learning community that deep learning models consume more energy compared to traditional models, but often are used because of their general applicability and sim. Promoting more energy efficient models where alternatives are available would have a considerable impact, if practiced universally.

‍

Efforts like these not only reduce carbon footprints but also make AI more accessible to smaller enterprises, which may not have the resources to run massive models.

‍

2. Sustainable Data Centres

‍

Building greener data centres is another essential strategy. Companies like Google and Microsoft are already investing in renewable energy to power their vast computing infrastructure. By committing to 100% renewable energy or offsetting their carbon emissions, these tech giants are setting a standard for others to follow.

‍

3. Inclusive AI Governance

‍

AI sustainability is not just a technological challenge — it’s also an ethical one. The rise of AI governance platforms, like oxethica, provides an opportunity for organisations to ensure their AI systems adhere to environmental, ethical, and regulatory standards. By setting clear guidelines for responsible AI, governance platforms help businesses navigate the complexities of sustainable development.

‍

The Role of AI in Promoting Sustainability

‍

AI is not just a contributor to environmental concerns; it also holds immense potential for advancing sustainability. From smart grids and optimised energy systems to precision agriculture and climate modelling, AI serves as a powerful ally in addressing climate change. Below are several examples of how AI can be leveraged to tackle pressing environmental issues.

‍

1. AI for Climate Monitoring

‍

One of the most promising applications of AI is its ability to analyse vast amounts of data to identify patterns and predict outcomes. In the context of climate change, AI can be used to track deforestation, monitor air quality, and forecast extreme weather events. By providing real-time insights, AI helps policymakers and scientists respond more effectively to environmental crises.

‍

2. Optimising Energy Consumption

‍

AI systems are increasingly being integrated into energy management frameworks. Smart grids, for example, use AI to optimise energy distribution, ensuring that renewable energy sources like wind and solar power are efficiently allocated. This can help reduce reliance on fossil fuels and minimise energy waste.

‍

Conclusion: Embracing Sustainable AI

‍

As the AI landscape evolves, the demand for sustainable AI will only increase. Companies that invest in responsible and sustainable AI technologies now will be better equipped to meet future challenges. Achieving sustainable AI requires a commitment to balancing innovation with environmental and social responsibility, as well as addressing concerns such as misuse of AI.

‍

At oxethica, we are dedicated to helping organisations reach these goals through our AI governance platform. Whether you are a tech company or a small business leader focused on ensuring ethical AI practices, oxethica offers tools like AI Auditing and AI Inventory Management to keep you ahead of the curve.

‍

FAQs on Sustainable AI

‍

What do you mean by sustainable AI?

‍

Sustainable AI refers to the development and use of artificial intelligence in ways that minimise environmental impact, promote energy efficiency, and ensure ethical, socially responsible practices.

‍

What is an example of AI for sustainable development?

‍

An example of AI for sustainable development is AI-powered precision farming. It analyses data on soil, weather, and crops, which helps farmers use resources efficiently, save water, and reduce pesticide use. The results in more sustainable farming practices.

‍

How can AI be used in sustainability?

‍

AI can support sustainability by enhancing energy efficiency through smart grids, predicting and managing renewable energy sources, monitoring deforestation, and improving waste management systems. It can also assist in climate modelling, enabling better forecasting of extreme weather events and environmental changes.

‍

What is eco AI?

‍

Eco AI refers to AI technologies and practices that are specifically designed to be environmentally friendly. This includes the development of energy-efficient AI models, the use of renewable energy to power data centres, and efforts to reduce the carbon footprint associated with AI training and deployment. Eco AI aims to ensure that AI's growth doesn’t come at the cost of the planet.

‍