AI and Environmental Impact

The fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income with respect to the development, implementation, and enforcement of environmental laws, regulations, and policies.

The recognition that digital technologies rely on physical resources like land, water, energy, and materials, and have significant environmental impacts.

The impact of physical location, geographic features, and human settlements on the environmental footprint of technology.

The evolution of computing's environmental impacts, including the use of resources, energy consumption, and waste generation.

The tendency for the negative environmental consequences of computing to be shifted onto marginalized communities or future generations.

AI systems are embedded within complex systems that include data, infrastructure, software, human practices, and regulations. Each of these elements contributes to the overall impact of AI on the environment.

Modern AI systems heavily rely on massive amounts of data, powerful processing, and specialized hardware, leading to significant energy consumption, e-waste generation, and potential environmental degradation.

The tendency to view environmental problems as needing technical solutions through AI might overlook the complex social and political dimensions involved in addressing these problems.

Advocates argue that AI can play a role in monitoring climate change, predicting disasters, promoting sustainable practices, and managing resources efficiently.

Despite its potential, AI's deployment in environmental contexts faces challenges like data biases, technological limitations, and potential unintended consequences.

While AI itself is often conceptualized as immaterial, its creation and operation require significant physical resources and energy, leading to ecological concerns related to materiality.

The potential of AI to improve environmental sustainability is significant, but requires careful consideration of the ethical, societal, and environmental implications of its development and deployment.

The amount of electricity used by data centers to power servers, cooling systems, and other infrastructure. It's a significant contributor to greenhouse gas emissions.

The environmental impact of training large language models (LLMs) like GPT-3, measured in carbon dioxide emissions. This includes energy use during the training process.

The connection between the disposal of electronic devices and the growth of artificial intelligence. AI systems contribute to increasing electronic waste through the production of devices and the obsolescence of older technology.

The raw materials needed to produce the components of AI systems, such as lithium, cobalt, and rare earth elements. Mining these minerals can have severe environmental and social impacts.

The fair and equitable distribution of the benefits and burdens of AI technology. This includes considering the environmental impacts and ensuring that marginalized communities are not disproportionately affected.

A core principle in environmental justice advocating for minimizing negative environmental impacts of AI advancements. It emphasizes ethical responsibility and seeks to mitigate harm caused by AI technologies.

Moving beyond simply minimizing harm, towards actively promoting positive environmental outcomes through AI. This involves using AI for solving environmental problems and sustainably developing technologies.

The overall impact of AI technologies on the environment, encompassing energy consumption, resource depletion, e-waste generation, and potential for contributing to climate change.