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.

翻译：随着人工智能（AI）的可能性不断扩展，其对社会与环境影响的担忧也日益增长。然而，这些问题往往被单独提出；例如，AI模型的碳足迹分析通常不考虑对规模的追求如何导致构建出既在成本上对大多数研究者难以企及、又对环境造成不成比例危害的模型。另一方面，旨在评估模型性能及差异影响的模型审计大多未能涉及AI模型的环境后果，以及这些后果如何融入其审计方法。在这种分离状态下，两个研究方向均未能捕捉到通过并行考虑二者所能探索的分析深度，以及在二者交汇处可能形成的、用于做出知情选择的潜在解决方案。本文基于AI及哲学与可持续发展等相关领域的研究成果，在AI研究与实践中围绕泛化性、透明度、评估与公平等主题建立更审慎的关联。我们认为，研究AI伦理影响的努力应与评估其环境影响的努力同步进行，并在文末提出最佳实践建议，以更好地将AI伦理与可持续性整合到AI研究与实践中。