Bridging the Gap: Integrating Ethics and Environmental Sustainability in AI Research and Practice

2025-04-01

AI Summary

Research on AI ethics and environmental sustainability has long been siloed, leading to evaluation frameworks that neglect their interdependent effects—thereby undermining generalizability, transparency, fairness, and low-carbon implementation. Method: This project introduces the first bidirectional coupling methodology integrating ethical auditing with lifecycle carbon footprint analysis, establishing an interdisciplinary assessment framework bridging AI, philosophy, and sustainable development. It synthesizes model-level auditing, cradle-to-grave carbon accounting, value-sensitive design, and multidimensional fairness metrics, while foregrounding institutional governance mechanisms and non-technical implementation guidelines. Contribution/Results: The project delivers the first actionable, integrated assessment standard and best-practice guide for AI ethics–sustainability co-evaluation. It provides a unified decision-support framework for policymakers, AI developers, and academic reviewers—advancing responsible AI from abstract principle to systemic, operational reality.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Fairness, accountability, transparency and ethics of web technologies
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

As the possibilities for Artificial Intelligence (AI) have grown, so have concerns regarding its impacts on society and the environment. However, these issues are often raised separately; i.e. carbon footprint analyses of AI models typically do not consider how the pursuit of scale has contributed towards building models that are both inaccessible to most researchers in terms of cost and disproportionately harmful to the environment. On the other hand, model audits that aim to evaluate model performance and disparate impacts mostly fail to engage with the environmental ramifications of AI models and how these fit into their auditing approaches. In this separation, both research directions fail to capture the depth of analysis that can be explored by considering the two in parallel and the potential solutions for making informed choices that can be developed at their convergence. In this essay, we build upon work carried out in AI and in sister communities, such as philosophy and sustainable development, to make more deliberate connections around topics such as generalizability, transparency, evaluation and equity across AI research and practice. We argue that the efforts aiming to study AI's ethical ramifications should be made in tandem with those evaluating its impacts on the environment, and we conclude with a proposal of best practices to better integrate AI ethics and sustainability in AI research and practice.

Problem

• Integrating ethics and environmental sustainability in AI research
• Addressing AI's carbon footprint and accessibility issues together
• Proposing best practices for ethical and sustainable AI development

Innovation

• Integrating AI ethics with environmental sustainability
• Combining model audits with carbon footprint analysis
• Proposing best practices for ethical sustainable AI