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https://ceur-ws.org/Vol-3871/paper8.pdf
Argumentation for Informed Decisions with Applications
to Energy Consumption in Computing
Pietro Baroni1 , Federico Cerutti1,2,∗ , Massimiliano Giacomin1 , Gian Franco Lamperti1 and
Marina Zanella1
1
DII - Universitá di Brescia - Italy
2
Cardiff University - UK
Abstract
The primary objective of the AIDECC (Argumentation for Informed Decisions with Applications to Energy
Consumption in Computing) project is to enhance energy efficiency in neural network training by integrating
argumentation-based causal discovery and machine learning. The project’s planned activities include a theoretical
investigation into abstract argumentation and its role in causal discovery, a detailed analysis of relevant datasets,
and the development of structural causal models for identifying energy reduction interventions.
Keywords
Computational Argumentation, Causal Models, Energy Efficiency, Neural Networks
1. Introduction
In recent years, Deep Neural Networks (DNNs) [1] have been extensively adopted across various
data-driven fields, including computer vision [2], natural language processing [3], personalised recom-
mendation [4], and speech recognition [5]. The growth in these areas has led to an increased reliance
on powerful, parallel GPU clusters for training DNN models. However, this surge in computational
demand has significant energy implications [6]. The (recently started) AIDECC project aims to achieve
a more sustainable approach to AI development, focusing on reducing energy consumption. To this
end, the project focuses on investigating causal relationships related to energy consumption in DNN
training. Through causal studies, the project seeks to design targeted interventions to reduce energy
use, thereby directly contributing to decreased waste and pollutant emissions associated with electricity
production.
2. Project Description
Three main objectives can be identified for the project. In accordance with the EU AI Act, the project
aims to devise a human-centric and ethically aligned approach, where the system’s decisions are always
subject to human review and interpretation.
A second key objective is acquiring a situational understanding of energy consumption in neural
network training, providing insights into the underlying mechanisms and their interactions. This means
identifying which variables affect energy use and understanding the causal relationships and rationale
behind these effects.
Finally, the project aims to identify strategies to reduce energy consumption and waste in training
neural networks. This involves a deep understanding of their computational and energy dynamics
along with an awareness of the practical constraints and operational environments in which these
networks are deployed. This analysis must consider real-world variables such as hardware limitations,
scalability, and the diverse nature of neural network applications across different industries.
To achieve these objectives, argumentation theory [7] is adopted as a fundamental component for
causal discovery. Within this paradigm, a causal link is considered a provisional argument subject to a
potential series of dialectical interactions [8]. For instance, the arguments from circumstantial evidence
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consider the spatial and temporal proximity of events, repeated co-occurrences, and similarities between
cause and effect. The arguments based on contrastive evidence rely on observations of covariation and
change, looking at how alterations in one event may result in changes in another. Also, arguments from
causal explanations delve into the underlying mechanisms and processes, considering the absence of
alternative explanations and the typical effects known to follow certain causes.
Argumentation fosters a balanced and comprehensive view, considering both the energy implica-
tions and the potential compromises in performance or accuracy, facilitating informed and sustainable
decision-making through a nuanced discussion (the first objective). It is also instrumental in enhancing
causal discovery, especially in scenarios with scarce data, unveiling the causal relationships between
the architectural choices and hyperparameters on learning process efficiency (the second objective).
Furthermore, argumentative causal models support potential interventions to reduce energy consump-
tion in DNN training while engaging in informed discussions and debates about various interventions’
merits and potential trade-offs (the third objective).
The project comprises three working packages.
WP1: Theoretical investigation of argumentative techniques for informed decision We plan
to expand upon the state-of-the-art on causal discovery and argumentation-based machine
learning to support informed human decision-making. In particular, WP1 will focus on the
theoretical foundations of argumentation and its application in identifying and evaluating causal
relationships from data. It will also explore integrating argumentative methods with machine
learning algorithms, illustrating how this combination can lead to more robust and transparent
models.
WP2: Argumentative analysis of neural network energy consumption We plan to analyse rel-
evant datasets1 using the advanced argumentative techniques developed as part of WP1.
WP3: Argumentative-causal reasoning to reduce DNN energy consumption The insights
gleaned from Work Packages 1 (WP1) and 2 (WP2) will be instrumental in developing a structural
causal model. This model will map out the causal relationships between different variables for
which we can build arguments supporting the claim that they influence energy consumption.
3. Expected Impact
The energy-intensive nature of training complex neural networks has significant environmental impli-
cations, primarily due to the substantial carbon footprint associated with high energy consumption.
Through causal studies, the project aims to formulate policies and design targeted interventions to
reduce energy use, thereby directly contributing to a decrease in pollutant emissions associated with
electricity production.
Acknowledgments
This work has been supported by the EU NEXTGENERATIONEU program within the PNRR Future
Artificial Intelligence - FAIR project (PE0000013, CUP H23C22000860006), Objective 10: Abstract
Argumentation for Knowledge Representation and Reasoning, specifically by the project Argumentation
for Informed Decisions with Applications to Energy Consumption in Computing—AIDECC (CUP
D53C24000530001).
References
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