=Paper=
{{Paper
|id=Vol-3637/paper36
|storemode=property
|title=Conceptual Foundations of Sustainability. A Sustainability Perspective on Artificial
Intelligence (extended abstract)
|pdfUrl=https://ceur-ws.org/Vol-3637/paper36.pdf
|volume=Vol-3637
|authors=Larissa Bolte
|dblpUrl=https://dblp.org/rec/conf/jowo/Bolte23
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==Conceptual Foundations of Sustainability. A Sustainability Perspective on Artificial
Intelligence (extended abstract)==
https://ceur-ws.org/Vol-3637/paper36.pdf
Conceptual Foundations of Sustainability. A Sustainability
Perspective on Artificial Intelligence: Extended Abstract
Larissa Bolte1
1 Institute for Science and Ethics, University of Bonn, Bonner Talweg 57, 53113 Bonn, Germany 1
1. Abstract
This project investigates the conceptual foundations of ‘sustainability’ with the goal of assessing
approaches to the ethics of Artificial Intelligence (AI) under the lens of that notion. In a previous
paper, my co-authors, Tijs Vandemeulebroucke and Aimee van Wynsberghe, and I have suspected
that in order to do justice to the normative demands of sustainability, the way in which we
conceive of AI ethics, AI regulation, and ultimately AI as a technology has to be adjusted [1].
The study of ‘Sustainable AI', i.e. of AI applications for sustainability and of the sustainability
of AI itself [2], is currently in its infancy. First publications in the field point to significant
environmental and social costs attached to the widespread adoption of AI technologies [3][4][5].
And yet, comprehensive frameworks for how these costs can be identified, assessed, and
evaluated are largely missing.
At the same time, a particular approach to AI policy crystallises – there is a tendency in AI
Ethics Guideline documents to focus on technical fixes for isolated artefacts, deterministically
construed, that lie in the responsibility of expert technicians – an approach my colleagues and I
have dubbed an “ethics of carefulness” [1]. For the most part, they do not consider broader
societal transformations, the embeddedness of AI technologies in social and ecological structures,
or the possibility of not developing a particular AI application at all.
By contrast, in the context of discourse on AI and sustainability, AI has increasingly been
conceptualised not as an artefact, but rather as infrastructure. This includes consideration of the
hardware infrastructure that is necessary to run AI algorithms [6][7], the fact that AI underpins
and upholds infrastructures [8][9], and that the interplay of AI algorithms and their environment
also constitutes an infrastructure in its own right [1][9]. Indeed, it has been argued that
conceptualising and assessing the sustainability of AI requires considering AI artefacts not in
isolation, but rather in their embeddedness in the broader ecological and socio-technical systems
that surround, enable, and constitute them [1][10].
It seems that sustainability is simply not ‘happening’ at the artefact level. This may explain
why social and ecological costs of AI, costs related to sustainability, are often described as
“hidden”[2][8]: Through the lens of an ethics of carefulness, they are invisible.
My research contributes a thorough examination of the normative demands inherent to the
sustainability perspective. These demands require modelling AI not as a particular artefact, but
rather as a socio-technical system embedded in social, environmental, and economic structures.
The normative demands of sustainability would thus require a different ontology for AI than the
one that is predominantly found in AI policy documents.
FOIS 2023 Early Career Symposium (ECS), held at FOIS 2023, co-located with 9th Joint Ontology Workshops (JOWO
2023), 19-20 July, 2023, Sherbrooke, Québec, Canada
bolte@iwe.uni-bonn.de (L. Bolte)
0000-0003-4989-9558 (L. Bolte)
© 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
2. Motivation
The notion ‘sustainability’ has risen to extraordinary relevance in the face of the current climate
crisis. Policy makers on all levels of government, businesses, research institutions, NGOs, and
individuals alike have made ‘sustainability’ their guiding concern. This trend now also extends to
current debates on digitalisation and, more specifically, Artificial Intelligence (AI).
First pointers to an environmental cost attached to AI applications have been provided by papers
assessing the energy consumption and associated greenhouse gas emissions produced by
training, tuning, and using AI systems. According to first estimates, the carbon emissions
produced by training, and even more so by tuning, just one Natural Language Processing model
may be considerable [3][11].
And yet, the environmental impact of AI extends beyond carbon emissions produced by the
energy consumption of algorithms in development and use. For AI systems to run, they require
instantiation in hardware and an industrial infrastructure to supply, maintain and replace this
hardware, the environmental impact of which is of yet to be fully assessed. Given that AI is in the
process of forming vital infrastructures that will shape our societies for decades to come [9],
suitable frameworks to steer this development into a sustainable direction are now timelier than
ever and direly needed. It is thus essential to understand what ‘Sustainable AI’ entails
conceptually, i.e. what empirical data is needed to assess the sustainability of AI, what normative
demands are supported by the data, and how we ought to conceptualise AI as a technology in light
of sustainability concerns.
In the AI ethics context, only few researchers have made first attempts at adapting the
sustainability notion for their purposes [1][2][5][6][7][9][10], and, ultimately, no comprehensive
sustainability framework has to date been proposed for AI ethics. It stands to reason that a
thorough examination of the sustainability concept within and outside of its employ in AI ethics
discourse will yield insights that will prove fruitful for anyone working on Sustainable AI from a
research or a regulation perspective.
3. Research Questions
3.1. Overarching Research Question
How can ‘sustainability’, construed as a theoretical lens, inform the way we conceive of
‘Sustainable AI’ as a new paradigm for AI ethics?
3.2. Phase 1 Research Questions
What are the central characteristics of ‘sustainability’?
What are the normative demands implied by or inherent to ‘sustainability’?
What ontology is required so ‘sustainability’ demands can be modelled?
3.3. Phase 2 Research Questions
What is the current state of AI ethics?
How does the sustainability framework developed in Research Phase 1 apply?
How can this framework inform or reform debates in AI ethics?
4. Objectives
4.1. Objectives of Phase 1 (Developing a Theoretical Sustainability
Framework)
• Scoping out the conceptual space of ‘sustainability’:
First, sustainability conceptualisations in the literature will be identified, grouped,
contrasted, and contextualised, with special focus on what aims, norms, goods, etc.
sustainability theorists posit and on how the world must be construed from a
sustainability perspective. A conceptual overview will be created.
• Developing a theoretical framework:
A theoretical framework is an analytical structure that is used for interpretation and
assessment. My project asks how AI ethics approaches can be interpreted and assessed
from a sustainability perspective, i.e. whether and how AI ethics approaches are capable
of answering to sustainability concerns. A sustainability framework will thus have to
identify these concerns. Furthermore, normative concepts cannot be understood without
the ontology on which they rely. The theoretical framework I develop will thus also have
to map out what model of the world ‘sustainability’ requires, i.e. what aspects of a
situation it picks out.
4.2. Objectives of Phase 2 (AI Ethics from a Sustainability Perspective):
• Identifying broader movements or paradigms in AI ethics:
Before the sustainability framework developed in Research Phase 1 can be applied to the
AI ethics context, the state of the latter must first be determined. Instead of giving a
comprehensive overview of singular issues, broader movements in AI ethics will be
identified.
• Revising our conceptualisation of AI as a technology from a sustainability perspective:
Ordinary conceptions of AI as a technology will be assessed and revised in light of
sustainability concerns.
5. Research Methodology
I work from a critical theory perspective and follow what Sally Haslanger calls a “revisionary
project” [12]: Such a project approaches the definition of concepts from a pragmatic needs-based
perspective. Revisionary projects amend concepts to turn them into effective tools to achieve
legitimate purposes. They ask: What iteration of this concept would serve our cognitive or
practical purposes best? Haslanger contrasts this kind of epistemic project with conceptual
projects, which explore and articulate the nuances of ordinary concepts, and with descriptive
projects, which study the extension of a concept to refine it.
In the context of the concepts of race and gender, in light of which Haslanger makes this
distinction, a descriptive project could investigate whether there are social kinds that are tracked
by our uses of race and gender vocabulary. A conceptual project would explore and articulate our
notions of race and gender as they are used. A revisionary project, however, asks how we should
use the concepts of race and gender if we want to achieve our goal of, for example, properly
addressing racial and sexual injustices.
In the context of my project, I ask: What are our practical purposes when engaging in
sustainability discourse? How ought we revise our conception of what AI is and how it interacts
with the world from a sustainability perspective?
One objective in joining the ECS at FOIS 2023 has been to explore methodologies for
investigating implicit ontological commitments in sustainability conceptions as well as for how
to deduce suitable ontologies from normative demands.
6. Research Results to Date
A first paper with the outlook that sustainability may require a systems ontology [1].
Acknowledgements
Funding for this research was provided by the Alexander von Humboldt Foundation in the
framework of the Alexander von Humboldt Professorship for Artificial Intelligence endowed by
the Federal Ministry and Research to Prof. Dr. Aimee van Wynsberghe.
References
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Carefulness to an Ethics of Desirability: Going Beyond Current Ethics Approaches to
Sustainable AI." Sustainability 14.8 (2022), 4472.
[2] van Wynsberghe, Aimee. "Sustainable AI: AI for sustainability and the sustainability of AI."
AI and Ethics 1.3 (2021), 213-218.
[3] E. Strubell, A. Ganesh, A. McCallum, Energy and Policy Considerations for Deep Learning in
NLP, in: Proceedings of the 57th Annual Meeting of the Association for Computational
Linguistics, Association for Computational Linguistics, Florence, Italy, 2019, pp. 3645-3650.
doi: 10.18653/v1/P19-1355.
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[6] K. Crawford, The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence,
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[7] M. M. D. Bolger, A. Tofighi-Niaki, L. Seelman, 'Green Mining’ is A Myth: The Case for Cutting
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[8] P. Dauvergne, AI in the Wild: Sustainability in the Age of Artificial Intelligence, The MIT Press,
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[9] Robbins, Scott and Aimee van Wynsberghe. "Our New Artificial Intelligence Infrastructure:
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[10] Sætra, Henrik Skaug. "AI in context and the sustainable development goals: Factoring in the
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[11] Lacoste, Alexandre, Alexandra Luccioni, Victor Schmidt and Thomas Dandres. "Quantifying
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[12] Haslanger, Sally. "Gender and Race: (What) Are They? (What) Do We Want Them To Be? ."
Noûs 34.1 (2000), 31-55.