AI and Social Justice in the Energy Transition

The challenge

AI is no longer experimental but operational in the energy system. From smart meters managing household energy to algorithms optimising renewable energy integration, AI systems are rapidly embedding into every aspect of our energy infrastructure.

The new AI-enabled energy system could continue policies and practices that exacerbate social justice concerns currently residing in today’s energy system. For instance, people in low-income households are unable to meet their energy needs at affordable prices. Algorithms trained on biased data could continue to make discriminatory decisions that prevent certain demographics or communities from accessing new products and services.

Without proactive governance or innovative applications, AI risks becoming a tool that optimises energy systems for efficiency and low emissions while entrenching inequality.

Our hypothesis

Given AI’s momentum in the renewable energy transition, Forum for the Future wanted to better understand how AI might accelerate social justice or exacerbate social injustice in the transition to renewable energy systems.

Through our research, we found that there is little examination of AI ethics and energy justice together. Extensive literature exists on AI ethics and on issues related to energy justice — who benefits, who decides, whose voices count, and how harms are repaired. We found that few studies examined how AI will specifically impact social justice in the renewable energy transition. We look to help bridge that gap by defining a taxonomy of social justice issues that can be used to further expound this critical intersection of AI and energy.

We believe there is a need to:

Acknowledge and mitigate the risk of AI deployment exacerbating and further embedding social injustices; and

Identify the opportunities that AI presents for centering positive social justice outcomes and redressing inequality in the existing energy system.

AI and Social Justice in the Energy Transition

Forum has created 12-issue taxonomy of social justice issues impacting and impacted by the deployment of AI in the renewable energy transition.

To do this, we conducted an AI-assisted literature review into AI ethics and energy justice frameworks mapping the stages of AI development and deployment and justice dimensions against social issues in both AI ethics frameworks and energy justice frameworks. This process identified 12 overlapping social justice issues which we validated against 70 real world cases and in nine expert interviews with academics, AI technology experts, energy companies, civil society, and government innovators familiar with the UK and Philippines markets.

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Whether you are conducting AI or energy-related research, assessing the merits of AI deployment, developing policy, organising communities, building new AI technologies or innovating new AI-enabled business models these 12 issues offer a guide to the questions that need to be asked and a sense of the potential unintended impacts that need exploring.

This white paper and taxonomy serve as a foundation for further work, which can be built on by:

Considering the different perspectives and priorities that stakeholders found in the taxonomy, and ranking the issues either by timeframe or impact;
Developing the taxonomy into a set of design principles or a practical tool that could better assist stakeholders to build fairness into plans, products and processes;
Developing a set of measurable indicators for the taxonomy;
Exploring how the taxonomy might incorporate intersectional analysis across gender, class, ethnicity or geography; and
Forming the basis of a set of scenarios or pathways that could help stakeholders to explore what AI for a just and regenerative renewable energy system might look like.

If you are a policy maker, sustainability leader or just transition advocate, we would love to discuss how you would deploy the taxonomy and what potential you see for social justice through the deployment of AI.

Or, if you have found this paper interesting and would like to work with us to further refine and test the taxonomy, please get in touch.

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