Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Data Ecofeminism

Ana Valdivia

TL;DR

Data Ecofeminism identifies GenAI's environmental costs—carbon, water, and e-waste—and critiques the North-dominated innovation race that increasingly relies on nuclear energy. It introduces seven principles grounded in Data Feminism and ecofeminist thought to reorient AI research toward climate justice, digital materiality awareness, metric transparency, frugal computing, digital sovereignty, commons, and pluriversal epistemologies. The framework aims to transform AI infrastructure and governance to prioritize life, people, and the planet, through public, shared, and diverse knowledge production. This work has practical implications for policy, industry reporting, and research culture, seeking to keep global warming within $1.5^{\circ}C$ while expanding care-centered technoscience.

Abstract

Generative Artificial Intelligence (GenAI) is driving significant environmental impacts. The rapid development and deployment of increasingly larger algorithmic models capable of analysing vast amounts of data are contributing to rising carbon emissions, water withdrawal, and waste generation. Generative models often consume substantially more energy than traditional models, with major tech firms increasingly turning to nuclear power to sustain these systems -- an approach that could have profound environmental consequences. This paper introduces seven data ecofeminist principles delineating a pathway for developing technological alternatives of eco-societal transformations within the AI research context. Rooted in data feminism and ecofeminist frameworks, which interrogate about the historical and social construction of epistemologies underlying the hegemonic development of science and technology that disrupt communities and nature, these principles emphasise the integration of social and environmental justice within a critical AI agenda. The paper calls for an urgent reassessment of the GenAI innovation race, advocating for ecofeminist algorithmic and infrastructural projects that prioritise and respect life, the people, and the planet.

Data Ecofeminism

TL;DR

Data Ecofeminism identifies GenAI's environmental costs—carbon, water, and e-waste—and critiques the North-dominated innovation race that increasingly relies on nuclear energy. It introduces seven principles grounded in Data Feminism and ecofeminist thought to reorient AI research toward climate justice, digital materiality awareness, metric transparency, frugal computing, digital sovereignty, commons, and pluriversal epistemologies. The framework aims to transform AI infrastructure and governance to prioritize life, people, and the planet, through public, shared, and diverse knowledge production. This work has practical implications for policy, industry reporting, and research culture, seeking to keep global warming within while expanding care-centered technoscience.

Abstract

Generative Artificial Intelligence (GenAI) is driving significant environmental impacts. The rapid development and deployment of increasingly larger algorithmic models capable of analysing vast amounts of data are contributing to rising carbon emissions, water withdrawal, and waste generation. Generative models often consume substantially more energy than traditional models, with major tech firms increasingly turning to nuclear power to sustain these systems -- an approach that could have profound environmental consequences. This paper introduces seven data ecofeminist principles delineating a pathway for developing technological alternatives of eco-societal transformations within the AI research context. Rooted in data feminism and ecofeminist frameworks, which interrogate about the historical and social construction of epistemologies underlying the hegemonic development of science and technology that disrupt communities and nature, these principles emphasise the integration of social and environmental justice within a critical AI agenda. The paper calls for an urgent reassessment of the GenAI innovation race, advocating for ecofeminist algorithmic and infrastructural projects that prioritise and respect life, the people, and the planet.

Paper Structure

This paper contains 16 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Background
  3. What is ecofeminism?
  4. The relationship between ecofeminism, science and technology
  5. Related work
  6. On the environmental impacts of data and AI technologies
  7. An ecofeminist approach towards data and AI
  8. Data Ecofeminism
  9. Principle 1: Examine the climate emergency and its power structures
  10. Principle 2: Consider digital materiality and its supply chains
  11. Principle 3: Report environmental metrics
  12. Principle 4: Prioritise frugal AI computing
  13. Principle 5: Reclaim digital sovereignty
  14. Principle 6: Prioritise the commons and mutual aid
  15. Principle 7: Weave the pluriverse
  16. ...and 1 more sections

Figures (2)

  • Figure 1: NVIDIA's chips for GenAI consume more energy than Georgia or Guatemala cbinsights2023nvidia.
  • Figure 2: Google's data centres are generally more sustainable in the Global North google2024environmental.