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

• URL: http://arxiv.org/abs/2504.00797v1
• Date: Tue, 01 Apr 2025 13:53:11 GMT
• Title: Bridging the Gap: Integrating Ethics and Environmental Sustainability in AI Research and Practice
• Authors: Alexandra Sasha Luccioni, Giada Pistilli, Raesetje Sefala, Nyalleng Moorosi,
• Abstract summary: 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.<n>We propose best practices to better integrate AI ethics and sustainability in AI research and practice.
• Score: 57.94036023167952
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• Human services organizations and the responsible integration of AI: Considering ethics and contextualizing risk(s) [0.0]
  Authors argue that ethical concerns about AI deployment vary significantly based on implementation context and specific use cases.<n>They propose a dimensional risk assessment approach that considers factors like data sensitivity, professional oversight requirements, and potential impact on client wellbeing.
  arXiv  Detail & Related papers  (2025-01-20T19:38:21Z)
• Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits [54.648819983899614]
  General purpose AI seems to have lowered the barriers for the public to use AI and harness its power. We introduce PARTICIP-AI, a framework for laypeople to speculate and assess AI use cases and their impacts.
  arXiv  Detail & Related papers  (2024-03-21T19:12:37Z)
• Investigating Responsible AI for Scientific Research: An Empirical Study [4.597781832707524]
  The push for Responsible AI (RAI) in such institutions underscores the increasing emphasis on integrating ethical considerations within AI design and development. This paper aims to assess the awareness and preparedness regarding the ethical risks inherent in AI design and development. Our results have revealed certain knowledge gaps concerning ethical, responsible, and inclusive AI, with limitations in awareness of the available AI ethics frameworks.
  arXiv  Detail & Related papers  (2023-12-15T06:40:27Z)
• Responsible AI Considerations in Text Summarization Research: A Review of Current Practices [89.85174013619883]
  We focus on text summarization, a common NLP task largely overlooked by the responsible AI community. We conduct a multi-round qualitative analysis of 333 summarization papers from the ACL Anthology published between 2020-2022. We focus on how, which, and when responsible AI issues are covered, which relevant stakeholders are considered, and mismatches between stated and realized research goals.
  arXiv  Detail & Related papers  (2023-11-18T15:35:36Z)
• The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice [64.29355073494125]
  This article aims to ground what we dub the "participatory turn" in AI design by synthesizing existing theoretical literature on participation. We articulate empirical findings concerning the current state of participatory practice in AI design based on an analysis of recently published research and semi-structured interviews with 12 AI researchers and practitioners.
  arXiv  Detail & Related papers  (2023-10-02T05:30:42Z)
• On the meaning of uncertainty for ethical AI: philosophy and practice [10.591284030838146]
  We argue that this is a significant way to bring ethical considerations into mathematical reasoning. We demonstrate these ideas within the context of competing models used to advise the UK government on the spread of the Omicron variant of COVID-19 during December 2021.
  arXiv  Detail & Related papers  (2023-09-11T15:13:36Z)
• The Ethics of AI Value Chains [0.6138671548064356]
  Researchers, practitioners, and policymakers with an interest in AI ethics need more integrative approaches for studying and intervening in AI systems. We review theories of value chains and AI value chains from the strategic management, service science, economic geography, industry, government, and applied research literature. We recommend three future directions that researchers, practitioners, and policymakers can take to advance more ethical practices across AI value chains.
  arXiv  Detail & Related papers  (2023-07-31T15:55:30Z)
• A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges [35.317637957059944]
  We review major trends in machine learning approaches that can address the sustainability problem of AI. We will highlight the major limitations of existing studies and propose potential research challenges and directions for the development of next generation of sustainable AI techniques.
  arXiv  Detail & Related papers  (2022-05-08T09:38:35Z)
• Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir" [76.44130385507894]
  This paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices. Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design.
  arXiv  Detail & Related papers  (2021-11-01T17:57:04Z)
• Beyond Fairness Metrics: Roadblocks and Challenges for Ethical AI in Practice [2.1485350418225244]
  We review practical challenges in building and deploying ethical AI at the scale of contemporary industrial and societal uses. We argue that a holistic consideration of ethics in the development and deployment of AI systems is necessary for building ethical AI in practice.
  arXiv  Detail & Related papers  (2021-08-11T18:33:17Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.