Next Article in Journal
ELM-Based Non-Singular Fast Terminal Sliding Mode Control Strategy for Vehicle Platoon
Next Article in Special Issue
Acknowledging Sustainability in the Framework of Ethical Certification for AI
Previous Article in Journal
A Novel Combined Model for Short-Term Emission Prediction of Airspace Flights Based on Machine Learning: A Case Study of China
Previous Article in Special Issue
Sustainable AI and Intergenerational Justice
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

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

1
Department of Philosophy, University of Vienna, 1010 Vienna, Austria
2
Gradient Zero, 1010 Vienna, Austria
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(7), 4019; https://doi.org/10.3390/su14074019
Submission received: 24 February 2022 / Revised: 11 March 2022 / Accepted: 22 March 2022 / Published: 29 March 2022

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 AI; artificial intelligence; sustainability; AI governance; ethics; ethical AI; differential privacy

Share and Cite

MDPI and ACS Style

Raper, R.; Boeddinghaus, J.; Coeckelbergh, M.; Gross, W.; Campigotto, P.; Lincoln, C.N. Sustainability Budgets: A Practical Management and Governance Method for Achieving Goal 13 of the Sustainable Development Goals for AI Development. Sustainability 2022, 14, 4019. https://doi.org/10.3390/su14074019

AMA Style

Raper R, Boeddinghaus J, Coeckelbergh M, Gross W, Campigotto P, Lincoln CN. 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

Chicago/Turabian Style

Raper, Rebecca, Jona Boeddinghaus, Mark Coeckelbergh, Wolfgang Gross, Paolo Campigotto, and Craig N. Lincoln. 2022. "Sustainability Budgets: A Practical Management and Governance Method for Achieving Goal 13 of the Sustainable Development Goals for AI Development" Sustainability 14, no. 7: 4019. https://doi.org/10.3390/su14074019

APA Style

Raper, R., Boeddinghaus, J., Coeckelbergh, M., Gross, W., Campigotto, P., & Lincoln, C. N. (2022). Sustainability Budgets: A Practical Management and Governance Method for Achieving Goal 13 of the Sustainable Development Goals for AI Development. Sustainability, 14(7), 4019. https://doi.org/10.3390/su14074019

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop