Over the past months, WAO has been working on a report called Harnessing AI for Environmental Justice for Friends of the Earth. Funded by Mozilla, the report is a deep dive into the landscape of Artificial Intelligence (AI) and what Big Tech’s newest hype means for activists and campaigners.
Read or download the full report here.
In this series, we pull out salient points from the report, providing a briefer look into social and power dynamics we considered as we tried to give activists insight into how they might utilise AI in their work. The report, and thus many of these excerpts, is cc-by Friends of the Earth.
In Part 1, we’ll look at the predominant narratives around AI that are influencing how the industry, and we, as activists, are thinking about AI. Then, in Part 2, we’ll look at the systemic complexities of our technology use in relation to the climate crisis and other justice driven movements. Part 3 will look into the seven principles we developed as part of the Friends of the Earth project. Finally, Part 4 explores how to be an activist in the world of AI given predominant narratives and system complexities.
- Understanding Predominant Narratives in AI
- Systemic complexities with AI
- Starting principles for the ethical use of AI (coming soon!)
- How to be an Activist in a World of AI (coming soon!)
Ready? Let’s get started!
The polarising narratives around AI
The rapid advancement of Generative AI has led to polarising views of AI in general, creating a binary framing that limits nuanced understanding and which hinders informed decision making. AI is either leading us to a dystopian future where current inequalities are exacerbated to the point of societal collapse, or it is seen as a utopian solution that will fix our problems if we just keep going. (Joseph Rowntree Foundation, 2024).
The techno-solutionism inherent in the utopian vision insists that technology alone will solve the complex social and environmental problems faced by our societies. This obscures reality on the ground. It ignores the agency and power of our social and political solutions. It obfuscates the hidden costs of AI technology, both environmental and social, and the reality of the power dynamics at play in our societies. Market forces and Big Tech companies often control this narrative, attempting to wriggle out of their responsibility to create technology that benefits all of us, rather than a rich few. (Coldicutt, 2024).
On the other hand, the dystopian view of AI ignores the immense power that sophisticated technologies have to change the way our world works. It dismisses our own agency and engagement. After all, it was only thirty-five years ago that the World Wide Web was invented, a project that was built as an open contribution to society as a whole. Despite global issues and challenges, we would argue that the Web has changed the world for the better: social movements benefit from global amplification; activists focused on local issues can receive support or engagement from anywhere; we can build collective power as we find each other through the Web.
The dystopian story insists that integrating AI into our societies will accelerate planetary destruction and lead to further societal divides, as Big Tech abuses communities and the planet in its never-ending quest for more money and power. But technologies can be designed for both good and bad purposes, and have both positive and negative outcomes.
Blanket narratives about the use of AI are not helpful in the current socio-political climate. It is important to understand that there are different kinds of AI, and to form nuanced opinions and policies based on context. (QA, 2024). Highlighting the differences between two predominant types of AI is illustrative:
- Predictive AI is aiding climate science research and helping to optimise energy efficiency. It is being used to analyse vast amounts of climate data and to create early warning systems for weather events or natural disasters. (Climate One, 2024). Predictive AI can be used to help analyse data on public sentiment, demographics, and online behaviour to make campaigns more effective.
- Generative AI is a type of artificial intelligence that can create “original” content, such as text, images, audio, video, or code, in response to user prompts or requests. (Stryker & Scapicchio, 2024). It is based not only on vast tranches of training data, but requires enormous amounts of computational power. In addition, each request made to such a system requires even more energy to generate an answer to the request. It is, indeed, much more resource intensive.
In our report, we focus on the use of generative AI within environmental justice and digital rights activist communities. There are people in these communities who believe that there can be no “ethical” use of generative AI due to the reality of who holds the power in our current approach to AI. We certainly respect and understand this perspective, acknowledging that resistance and refusal are important pillars in activism, and wish only to augment that perspective with one that can direct our collective agency and power towards change.
In doing so, we aim to create a new story for AI — somewhere in between dystopia and utopia, that empowers us all to live our values while utilising AI for good.
These excerpts have been edited in the final report and everything is CC-BY Friends of the Earth and We Are Open Co-op. Read or download the full report here.
References:
Joseph Rowntree Foundation (2024) AI and the power of narratives. Available at: https://www.jrf.org.uk/ai-for-public-good/ai-and-the-power-of-narratives (Accessed: 24 October 2024).
Coldicutt, R. (2024). Let’s make AI work for 8 billion people not 8 billionaires. Scottish AI Summit. Available at: https://www.scottishaisummit.com/rachel-coldicutt (Accessed 9 December 2024).
QA (2024). Understanding the different types of AI. Available at: https://www.qa.com/resources/blog/types-of-ai-explained/ (Accessed: 22 November 2024).
Climate One (2024) ‘Artificial Intelligence, Real Climate Impacts’ [podcast], 19th April. Available at: https://www.climateone.org/audio/artificial-intelligence-real-climate-impacts (Accessed: 24 October 2024).
Stryker, C. & Scapicchio, M. (2024). What is generative AI? IBM Explainers. Available at: https://www.ibm.com/topics/generative-ai (Accessed: 22 November 2024).