[Paper Review] Towards an Environmental Ethics of Artificial Intelligence

Nynke van Uffelen, Lode Lauwaert+2 more|ArXiv.org|Dec 19, 2024

Ethics and Social Impacts of AI4 citations

TL;DR

This paper proposes an environmental ethics framework for AI by integrating environmental justice principles—distributive, procedural, and recognition justice—into AI design. It argues that ethical AI development must account for ecological impacts on non-human actors, offering criteria to assess when AI systems are environmentally justifiable based on energy use, decision-making fairness, and institutional norms.

ABSTRACT

In recent years, much research has been dedicated to uncovering the environmental impact of Artificial Intelligence (AI), showing that training and deploying AI systems require large amounts of energy and resources, and the outcomes of AI may lead to decisions and actions that may negatively impact the environment. This new knowledge raises new ethical questions, such as: When is it (un)justifiable to develop an AI system, and how to make design choices, considering its environmental impact? However, so far, the environmental impact of AI has largely escaped ethical scrutiny, as AI ethics tends to focus strongly on themes such as transparency, privacy, safety, responsibility, and bias. Considering the environmental impact of AI from an ethical perspective expands the scope of AI ethics beyond an anthropocentric focus towards including more-than-human actors such as animals and ecosystems. This paper explores the ethical implications of the environmental impact of AI for designing AI systems by drawing on environmental justice literature, in which three categories of justice are distinguished, referring to three elements that can be unjust: the distribution of benefits and burdens (distributive justice), decision-making procedures (procedural justice), and institutionalized social norms (justice as recognition). Based on these tenets of justice, we outline criteria for developing environmentally just AI systems, given their ecological impact.

Motivation & Objective

• To address the ethical oversight of AI’s environmental impact, which has been neglected in mainstream AI ethics.
• To expand AI ethics beyond anthropocentric concerns like bias and privacy to include non-human entities such as ecosystems and animals.
• To develop a framework for evaluating AI systems based on their ecological consequences using environmental justice theory.
• To provide actionable criteria for designing AI systems that minimize environmental harm and promote ecological fairness.
• To integrate distributive, procedural, and recognition justice into AI governance and development practices.

Proposed method

• Drawing on environmental justice literature, the paper identifies three pillars: distributive justice (fair distribution of environmental burdens and benefits), procedural justice (inclusive decision-making processes), and justice as recognition (respect for diverse ecological values).
• The authors apply these three categories to assess the environmental impact of AI systems across their lifecycle, from training to deployment.
• They develop a set of evaluative criteria for AI design that incorporate ecological consequences into ethical decision-making frameworks.
• The method involves conceptual analysis and normative reasoning, using ethical theory to guide technical and policy recommendations.
• The framework is applied to real-world AI development scenarios to assess environmental fairness and sustainability.
• The approach emphasizes stakeholder inclusion and institutional accountability in AI governance to ensure ecological considerations are systematically addressed.

Experimental results

Research questions

• RQ1 When is it ethically justifiable to develop or deploy an AI system given its environmental impact?
• RQ2 How can environmental justice principles be operationalized in the design and governance of AI systems?
• RQ3 What role do non-human entities—such as ecosystems and animals—play in ethical assessments of AI?
• RQ4 How can procedural fairness in AI decision-making be extended to include ecological stakeholders?
• RQ5 In what ways do existing institutional norms in AI development fail to recognize ecological interdependence?

Key findings

• The environmental impact of AI, particularly in energy and resource consumption, constitutes a significant ethical concern that has been underexplored in current AI ethics discourse.
• Integrating environmental justice into AI ethics enables a broader ethical scope that includes non-human actors such as animals and ecosystems.
• Distributive justice in AI requires assessing the equitable distribution of environmental costs and benefits across communities and ecosystems.
• Procedural justice in AI demands inclusive processes that involve ecological stakeholders in decisions about AI deployment and design.
• Recognition justice calls for institutional norms that acknowledge the intrinsic value of nature and ecological interdependence in AI governance.
• The proposed framework provides a normative foundation for evaluating AI systems based on their ecological sustainability and fairness, offering criteria for ethical design.