Sustainability Budgets: A Practical Management and Governance Method for Achieving Goal 13 of the Sustainable Development Goals for AI Development

Rebecca Raper, Jona Boeddinghaus, Mark Coeckelbergh, Wolfgang Gross, Paolo Campigotto and Craig N. Lincoln
Additional contact information
Rebecca Raper: Department of Philosophy, University of Vienna, 1010 Vienna, Austria
Jona Boeddinghaus: Gradient Zero, 1010 Vienna, Austria
Mark Coeckelbergh: Department of Philosophy, University of Vienna, 1010 Vienna, Austria
Wolfgang Gross: Gradient Zero, 1010 Vienna, Austria
Paolo Campigotto: Gradient Zero, 1010 Vienna, Austria
Craig N. Lincoln: Gradient Zero, 1010 Vienna, Austria

Sustainability, 2022, vol. 14, issue 7, 1-11

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 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.

Keywords: AI; artificial intelligence; sustainability; AI governance; ethics; ethical AI; differential privacy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/7/4019/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/7/4019/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:7:p:4019-:d:781919

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().