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Special Issue "Towards the Sustainability of AI; Multi-Disciplinary Approaches to Investigate the Hidden Costs of AI"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 13127

Special Issue Editors

Prof. Dr. Aimee van Wynsberghe
E-Mail Website
Leading Guest Editor
Institute for Science and Ethics, 53113 Bonn, Germany
Interests: artificial intelligence ethics; robot ethics; care ethics; value sensitive design; digital ethics
Ms. Larissa Bolte
E-Mail
Guest Editor Assistant
Institute for Science and Ethics, 53113 Bonn, Germany
Interests: ethics; metaethics; normativity; philosophy of logic
Ms. Jamila Nachid
E-Mail
Guest Editor Assistant
Institute for Science and Ethics, 53113 Bonn, Germany
Interests: bioethics; climate ethics; ethics of human rights; ethics of migration
Dr. Tijs Vandemeulebroucke
E-Mail Website
Guest Editor
Institute for Science and Ethics, 53111 Bonn, Germany
Interests: care ethics; bioethics; ethics of technology; philosophy of technology; phenomenology; deconstruction

Special Issue Information

Dear Colleagues,

This Special Issue will be published as an accompaniment to the world’s first conference dedicated to the topic of sustainable AI, organized by the University of Bonn, Germany. Sustainable AI can be understood as having two branches; AI for sustainability and sustainability of AI (e.g., reduction of carbon emissions and computing power).  In order to fully understand the benefits and risks of AI, it is important to investigate both of these branches. While there is a growing number of publications directed towards AI for the Sustainable Development Goals, there is little research addressing the, often hidden, environmental costs of AI. For this reason, Prof van Wynsberghe invites researchers of all relevant fields to start thinking about the sustainability of AI and aims at sparking discussion on the environmental, social and economic costs of designing, developing and using AI across society. The questions to be explored span across multiple disciplines and levels of analysis, for example: the normative grounding of the value of sustainability; the strength of the concept of sustainability; how to measure environmental costs of AI; understanding the intergenerational impacts of AI; and, informing public policy guidelines for the green, proportionate and sustainable development and use of AI.

Contributions from the perspectives of: computer sciences, philosophy, (applied) AI ethics, social sciences, law and policy, and others are welcome and encouraged.

Interested researchers are invited to address any of the following topics (list not exclusive):

  • How to measure, or experiences with measuring, carbon emissions of training and tuning AI models
  • Green AI
  • AI and smart cities
  • AI and sustainability impact assessments
  • AI and sustainable policy
  • AI and human rights
  • Sustainable AI and power dynamics
  • AI and intergenerational justice
  • Normative grounding of sustainability as a value to steer AI

Prof. Dr. Aimee van Wynsberghe
Ms. Larissa Bolte
Ms. Jamila Nachid
Dr. Tijs Vandemeulebroucke
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sustainability
  • artificial intelligence
  • sustainable AI
  • green AI
  • environmental impact
  • intergenerational justice
  • proportionality
  • impact assessment
  • public policy
  • sustainable development

Published Papers (12 papers)

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Research

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Article
Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions
Sustainability 2022, 14(9), 5172; https://doi.org/10.3390/su14095172 - 25 Apr 2022
Viewed by 331
Abstract
In the past ten years, artificial intelligence has encountered such dramatic progress that it is now seen as a tool of choice to solve environmental issues and, in the first place, greenhouse gas emissions (GHG). At the same time, the deep learning community [...] Read more.
In the past ten years, artificial intelligence has encountered such dramatic progress that it is now seen as a tool of choice to solve environmental issues and, in the first place, greenhouse gas emissions (GHG). At the same time, the deep learning community began to realize that training models with more and more parameters require a lot of energy and, as a consequence, GHG emissions. To our knowledge, questioning the complete net environmental impacts of AI solutions for the environment (AI for Green) and not only GHG, has never been addressed directly. In this article, we propose to study the possible negative impacts of AI for Green. First, we review the different types of AI impacts; then, we present the different methodologies used to assess those impacts and show how to apply life cycle assessment to AI services. Finally, we discuss how to assess the environmental usefulness of a general AI service and point out the limitations of existing work in AI for Green. Full article
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Article
The Ethics of AI-Powered Climate Nudging—How Much AI Should We Use to Save the Planet?
Sustainability 2022, 14(9), 5153; https://doi.org/10.3390/su14095153 - 24 Apr 2022
Viewed by 415
Abstract
The number of areas in which artificial intelligence (AI) technology is being employed increases continually, and climate change is no exception. There are already growing efforts to encourage people to engage more actively in sustainable environmental behavior, so-called “green nudging”. Nudging in general [...] Read more.
The number of areas in which artificial intelligence (AI) technology is being employed increases continually, and climate change is no exception. There are already growing efforts to encourage people to engage more actively in sustainable environmental behavior, so-called “green nudging”. Nudging in general is a widespread policymaking tool designed to influence people’s behavior while preserving their freedom of choice. Given the enormous challenges humanity is facing in fighting climate change, the question naturally arises: Why not combine the power of AI and the effectiveness of nudging to get people to behave in more climate-friendly ways? However, nudging has been highly controversial from the very beginning because critics fear it undermines autonomy and democracy. In this article I investigate the ethics of AI-powered climate nudging and address the question whether implementing corresponding policies may represent hidden and unacceptable costs of AI in the form of a substantive damage to autonomy and democracy. I will argue that, although there are perfectly legitimate concerns and objections against certain forms of nudging, AI-powered climate nudging can be ethically permissible under certain conditions, namely if the nudging practice takes the form of what I will call “self-governance”. Full article
Article
Our New Artificial Intelligence Infrastructure: Becoming Locked into an Unsustainable Future
Sustainability 2022, 14(8), 4829; https://doi.org/10.3390/su14084829 - 18 Apr 2022
Viewed by 360
Abstract
Artificial intelligence (AI) is becoming increasingly important for the infrastructures that support many of society’s functions. Transportation, security, energy, education, the workplace, the government have all incorporated AI into their infrastructures for enhancement and/or protection. In this paper, we argue that not only [...] Read more.
Artificial intelligence (AI) is becoming increasingly important for the infrastructures that support many of society’s functions. Transportation, security, energy, education, the workplace, the government have all incorporated AI into their infrastructures for enhancement and/or protection. In this paper, we argue that not only is AI seen as a tool for augmenting existing infrastructures, but AI itself is becoming an infrastructure that many services of today and tomorrow will depend upon. Considering the vast environmental consequences associated with the development and use of AI, of which the world is only starting to learn, the necessity of addressing AI alongside the concept of infrastructure points toward the phenomenon of carbon lock-in. Carbon lock-in refers to society’s constrained ability to reduce carbon emissions technologically, economically, politically, and socially. These constraints are due to the inherent inertia created by entrenched technological, institutional, and behavioral norms. That is, the drive for AI adoption in virtually every sector of society will create dependencies and interdependencies from which it will be hard to escape. The crux of this paper boils down to this: in conceptualizing AI as infrastructure we can recognize the risk of lock-in, not just carbon lock-in but lock-in as it relates to all the physical needs to achieve the infrastructure of AI. This does not exclude the possibility of solutions arising with the rise of these technologies; however, given these points, it is of the utmost importance that we ask inconvenient questions regarding these environmental costs before becoming locked into this new AI infrastructure. Full article
Article
From an Ethics of Carefulness to an Ethics of Desirability: Going Beyond Current Ethics Approaches to Sustainable AI
Sustainability 2022, 14(8), 4472; https://doi.org/10.3390/su14084472 - 08 Apr 2022
Viewed by 419
Abstract
‘Sustainable AI’ sets itself apart from other AI ethics frameworks by its inherent regard for the ecological costs of AI, a concern that has so far been woefully overlooked in the policy space. Recently, two German-based research and advocacy institutions have published a [...] Read more.
‘Sustainable AI’ sets itself apart from other AI ethics frameworks by its inherent regard for the ecological costs of AI, a concern that has so far been woefully overlooked in the policy space. Recently, two German-based research and advocacy institutions have published a joint report on Sustainability Criteria for Artificial Intelligence. This is, to our knowledge, the first AI ethics document in the policy space that puts sustainability at the center of its considerations. We take this as an opportunity to highlight the foundational problems we see in current debates about AI ethics guidelines. Although we do believe the concept of sustainability has the potential to introduce a paradigm shift, we question whether the suggestions and conceptual grounding found in this report have the strength to usher it in. We show this by presenting this new report as an example of current approaches to AI ethics and identify the problems of this approach, which we will describe as ‘checklist ethics’ and ‘ethics of carefulness’. We argue to opt for an ‘ethics of desirability’ approach. This can be completed, we suggest, by reconceptualizing sustainability as a property of complex systems. Finally, we offer a set of indications for further research. Full article
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Article
Acknowledging Sustainability in the Framework of Ethical Certification for AI
Sustainability 2022, 14(7), 4157; https://doi.org/10.3390/su14074157 - 31 Mar 2022
Viewed by 607
Abstract
In the past few years, many stakeholders have begun to develop ethical and trustworthiness certification for AI applications. This study furnishes the reader with a discussion of the philosophical arguments that impel the need to include sustainability, in its different forms, among the [...] Read more.
In the past few years, many stakeholders have begun to develop ethical and trustworthiness certification for AI applications. This study furnishes the reader with a discussion of the philosophical arguments that impel the need to include sustainability, in its different forms, among the audit areas of ethical AI certification. We demonstrate how sustainability might be included in two different types of ethical impact assessment: assessment certifying the fulfillment of minimum ethical requirements and what we describe as nuanced assessment. The paper focuses on the European, and especially the German, context, and the development of certification for AI. Full article
Article
Sustainability Budgets: A Practical Management and Governance Method for Achieving Goal 13 of the Sustainable Development Goals for AI Development
Sustainability 2022, 14(7), 4019; https://doi.org/10.3390/su14074019 - 29 Mar 2022
Viewed by 623
Abstract
Climate change is a global priority. In 2015, the United Nations (UN) outlined its Sustainable Development Goals (SDGs), which stated that taking urgent action to tackle climate change and its impacts was a key priority. The 2021 World Climate Summit finished with calls [...] Read more.
Climate change is a global priority. In 2015, the United Nations (UN) outlined its Sustainable Development Goals (SDGs), which stated that taking urgent action to tackle climate change and its impacts was a key priority. The 2021 World Climate Summit finished with calls for governments to take tougher measures towards reducing their carbon footprints. However, it is not obvious how governments can make practical implementations to achieve this goal. One challenge towards achieving a reduced carbon footprint is gaining awareness of how energy exhaustive a system or mechanism is. Artificial Intelligence (AI) is increasingly being used to solve global problems, and its use could potentially solve challenges relating to climate change, but the creation of AI systems often requires vast amounts of, up front, computing power, and, thereby, it can be a significant contributor to greenhouse gas emissions. If governments are to take the SDGs and calls to reduce carbon footprints seriously, they need to find a management and governance mechanism to (i) audit how much their AI system ‘costs’ in terms of energy consumption and (ii) incentivise individuals to act based upon the auditing outcomes, in order to avoid or justify politically controversial restrictions that may be seen as bypassing the creativity of developers. The idea is thus to find a practical solution that can be implemented in software design that incentivises and rewards and that respects the autonomy of developers and designers to come up with smart solutions. This paper proposes such a sustainability management mechanism by introducing the notion of ‘Sustainability Budgets’—akin to Privacy Budgets used in Differential Privacy—and by using these to introduce a ‘Game’ where participants are rewarded for designing systems that are ‘energy efficient’. Participants in this game are, among others, the Machine Learning developers themselves, which is a new focus for this problem that this text introduces. The paper later expands this notion to sustainability management in general and outlines how it might fit into a wider governance framework. Full article
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Article
Sustainable AI and Intergenerational Justice
Sustainability 2022, 14(7), 3922; https://doi.org/10.3390/su14073922 - 26 Mar 2022
Cited by 1 | Viewed by 512
Abstract
Recently, attention has been drawn to the sustainability of artificial intelligence (AI) in terms of environmental costs. However, sustainability is not tantamount to the reduction of environmental costs. By shifting the focus to intergenerational justice as one of the constitutive normative pillars of [...] Read more.
Recently, attention has been drawn to the sustainability of artificial intelligence (AI) in terms of environmental costs. However, sustainability is not tantamount to the reduction of environmental costs. By shifting the focus to intergenerational justice as one of the constitutive normative pillars of sustainability, the paper identifies a reductionist view on the sustainability of AI and constructively contributes a conceptual extension. It further develops a framework that establishes normative issues of intergenerational justice raised by the uses of AI. The framework reveals how using AI for decision support to policies with long-term impacts can negatively affect future persons. In particular, the analysis demonstrates that uses of AI for decision support to policies of environmental protection or climate mitigation include assumptions about social discounting and future persons’ preferences. These assumptions are highly controversial and have a significant influence on the weight assigned to the potentially detrimental impacts of a policy on future persons. Furthermore, these underlying assumptions are seldom transparent within AI. Subsequently, the analysis provides a list of assessment questions that constitutes a guideline for the revision of AI techniques in this regard. In so doing, insights about how AI can be made more sustainable become apparent. Full article
Article
The Environmental Sustainability of Digital Technologies: Stakeholder Practices and Perspectives
Sustainability 2022, 14(7), 3791; https://doi.org/10.3390/su14073791 - 23 Mar 2022
Viewed by 655
Abstract
Artificial Intelligence and associated digital technologies (DTs) have environmental impacts. These include heavy carbon dioxide emissions linked to the energy consumption required to generate and process large amounts of data; extracting minerals for, and manufacturing of, technological components; and e-waste. These environmental impacts [...] Read more.
Artificial Intelligence and associated digital technologies (DTs) have environmental impacts. These include heavy carbon dioxide emissions linked to the energy consumption required to generate and process large amounts of data; extracting minerals for, and manufacturing of, technological components; and e-waste. These environmental impacts are receiving increasing policy and media attention through discourses of environmental sustainability. At the same time, ‘sustainability’ is a complex and nebulous term with a multiplicity of meanings and practices. This paper explores how experts working with DTs understand and utilise the concept of environmental sustainability in their practices. Our research question was how do stakeholders researching, governing or working on the environmental impacts of DTs, utilise environmental sustainability concepts? We applied a combination of bibliometric analysis and 24 interviews with key stakeholders from the digital technology sector. Findings show that, although stakeholders have broad conceptual understandings of the term sustainability and its relation to the environmental impacts of DTs, in practice, environmental sustainability tends to be associated with technology based and carboncentric approaches. While narrowing conceptual understandings of environmental sustainability was viewed to have a practical purpose, it hid broader sustainability concerns. We urge those in the field not to lose sight of the wider ‘ethos of sustainability’. Full article
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Article
Mindful Application of Digitalization for Sustainable Development: The Digitainability Assessment Framework
Sustainability 2022, 14(5), 3114; https://doi.org/10.3390/su14053114 - 07 Mar 2022
Viewed by 743
Abstract
Digitalization is widely recognized as a transformative power for sustainable development. Careful alignment of progress made by digitalization with the globally acknowledged Sustainable Development Goals (SDGs) is crucial for inclusive and holistic sustainable development in the digital era. However, limited reference has been [...] Read more.
Digitalization is widely recognized as a transformative power for sustainable development. Careful alignment of progress made by digitalization with the globally acknowledged Sustainable Development Goals (SDGs) is crucial for inclusive and holistic sustainable development in the digital era. However, limited reference has been made in SDGs about harnessing the opportunities offered by digitalization capabilities. Moreover, research on inhibiting or enabling effects of digitalization considering its multi-faceted interlinkages with the SDGs and their targets is fragmented. There are only limited instances in the literature examining and categorizing the impact of digitalization on sustainable development. To overcome this gap, this paper introduces a new Digitainability Assessment Framework (DAF) for context-aware practical assessment of the impact of the digitalization intervention on the SDGs. The DAF facilitates in-depth assessment of the many diverse technical, social, ethical, and environmental aspects of a digital intervention by systematically examining its impact on the SDG indicators. Our approach draws on and adapts concepts of the Theory of Change (ToC). The DAF should support developers, users as well policymakers by providing a 360-degree perspective on the impact of digital services or products, as well as providing hints for its possible improvement. We demonstrate the application of the DAF with the three test case studies illustrating how it supports in providing a holistic view of the relation between digitalization and SDGs. Full article
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Article
Sustainability of AI: The Case of Provision of Information to Consumers
Sustainability 2021, 13(21), 12064; https://doi.org/10.3390/su132112064 - 01 Nov 2021
Cited by 1 | Viewed by 849
Abstract
The potential of artificial intelligence (AI) and its manifold applications have fueled the discussion around how AI can be used to facilitate sustainable objectives. However, the technical, ethical, and legal literature on how AI, including its design, training, implementation, and use can be [...] Read more.
The potential of artificial intelligence (AI) and its manifold applications have fueled the discussion around how AI can be used to facilitate sustainable objectives. However, the technical, ethical, and legal literature on how AI, including its design, training, implementation, and use can be sustainable, is rather limited. At the same time, consumers incrementally pay more attention to sustainability information, whereas businesses are increasingly engaging in greenwashing practices, especially in relation to digital products and services, raising concerns about the efficiency of the existing consumer protection framework in this regard. The objective of this paper is to contribute to the discussion toward sustainable AI from a legal and consumer protection standpoint while focusing on the environmental and societal pillar of sustainability. After analyzing the multidisciplinary literature available on the topic of the environmentally sustainable AI lifecycle, as well as the latest EU policies and initiatives regarding consumer protection and sustainability, we will examine whether the current consumer protection framework is sufficient to promote sharing and substantiation of sustainability information in B2C contracts involving AI products and services. Moreover, we will assess whether AI-related AI initiatives can promote a sustainable AI development. Finally, we will propose a set of recommendations capable of encouraging a sustainable and environmentally-conscious AI lifecycle while enhancing information transparency among stakeholders, aligning the various EU policies and initiatives, and ultimately empowering consumers. Full article

Review

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Review
A Survey on Sustainable Surrogate-Based Optimisation
Sustainability 2022, 14(7), 3867; https://doi.org/10.3390/su14073867 - 24 Mar 2022
Viewed by 718
Abstract
Surrogate-based optimisation (SBO) algorithms are a powerful technique that combine machine learning and optimisation to solve expensive optimisation problems. This type of problem appears when dealing with computationally expensive simulators or algorithms. By approximating the expensive part of the optimisation problem with a [...] Read more.
Surrogate-based optimisation (SBO) algorithms are a powerful technique that combine machine learning and optimisation to solve expensive optimisation problems. This type of problem appears when dealing with computationally expensive simulators or algorithms. By approximating the expensive part of the optimisation problem with a surrogate, the number of expensive function evaluations can be reduced. This paper defines sustainable SBO, which consists of three aspects: applying SBO to a sustainable application, reducing the number of expensive function evaluations, and considering the computational effort of the machine learning and optimisation parts of SBO. The paper reviews sustainable applications that have successfully applied SBO over the past years, and analyses the used framework, type of surrogate used, sustainable SBO aspects, and open questions. This leads to recommendations for researchers working on sustainability-related applications who want to apply SBO, as well as recommendations for SBO researchers. It is argued that transparency of the computation resources used in the SBO framework, as well as developing SBO techniques that can deal with a large number of variables and objectives, can lead to more sustainable SBO. Full article
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Other

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Perspective
A Framework for Evaluating and Disclosing the ESG Related Impacts of AI with the SDGs
Sustainability 2021, 13(15), 8503; https://doi.org/10.3390/su13158503 - 29 Jul 2021
Cited by 5 | Viewed by 3583
Abstract
Artificial intelligence (AI) now permeates all aspects of modern society, and we are simultaneously seeing an increased focus on issues of sustainability in all human activities. All major corporations are now expected to account for their environmental and social footprint and to disclose [...] Read more.
Artificial intelligence (AI) now permeates all aspects of modern society, and we are simultaneously seeing an increased focus on issues of sustainability in all human activities. All major corporations are now expected to account for their environmental and social footprint and to disclose and report on their activities. This is carried out through a diverse set of standards, frameworks, and metrics related to what is referred to as ESG (environment, social, governance), which is now, increasingly often, replacing the older term CSR (corporate social responsibility). The challenge addressed in this article is that none of these frameworks sufficiently capture the nature of the sustainability related impacts of AI. This creates a situation in which companies are not incentivised to properly analyse such impacts. Simultaneously, it allows the companies that are aware of negative impacts to not disclose them. This article proposes a framework for evaluating and disclosing ESG related AI impacts based on the United Nation’s Sustainable Development Goals (SDG). The core of the framework is here presented, with examples of how it forces an examination of micro, meso, and macro level impacts, a consideration of both negative and positive impacts, and accounting for ripple effects and interlinkages between the different impacts. Such a framework helps make analyses of AI related ESG impacts more structured and systematic, more transparent, and it allows companies to draw on research in AI ethics in such evaluations. In the closing section, Microsoft’s sustainability reporting from 2018 and 2019 is used as an example of how sustainability reporting is currently carried out, and how it might be improved by using the approach here advocated. Full article
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