The primary objective of the AIDECC (Argumentation for Informed Decisions with Applications to Energy Consumption in Computing) project is to enhance energy eficiency 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.
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 afect energy use and understanding the causal relationships and rationale behind these efects.
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 diferent 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 consider the spatial and temporal proximity of events, repeated co-occurrences, and similarities between cause and efect. 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 efects known to follow certain causes.
Argumentation fosters a balanced and comprehensive view, considering both the energy implications 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 eficiency (the second objective). Furthermore, argumentative causal models support potential interventions to reduce energy consumption in DNN training while engaging in informed discussions and debates about various interventions’ merits and potential trade-ofs (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.
evant datasets1 using the advanced argumentative techniques developed as part of WP1.
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 diferent variables for which we can build arguments supporting the claim that they influence energy consumption.
The energy-intensive nature of training complex neural networks has significant environmental implications, 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.
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). 1E.g.., the Zeus Power Trace, which ofers a detailed profiling of the power consumption, training duration, and other critical metrics across diferent network and dataset pairs under varying batch sizes and GPU power limits. [2] J. Chai, H. Zeng, A. Li, E. W. Ngai, Deep learning in computer vision: A critical review of emerging techniques and application scenarios, Machine Learning with Applications 6 (2021) 100134. [3] M. A. K. Raiaan, M. S. H. Mukta, K. Fatema, N. M. Fahad, S. Sakib, M. M. J. Mim, J. Ahmad, M. E. Ali, S. Azam, A review on large language models: Architectures, applications, taxonomies, open issues and challenges, IEEE Access (2024). [4] R. Zheng, L. Qu, B. Cui, Y. Shi, H. Yin, AutoML for deep recommender systems: A survey, ACM
Transactions on Information Systems 41 (2023) 1–38. [5] R. Prabhavalkar, T. Hori, T. N. Sainath, R. Schlüter, S. Watanabe, End-to-end speech recognition: A survey, IEEE/ACM Transactions on Audio, Speech, and Language Processing (2023). [6] D. Geißler, B. Zhou, M. Liu, S. Suh, P. Lukowicz, The power of training: How diferent neural network setups influence the energy demand, in: International Conference on Architecture of Computing Systems, Springer, 2024, pp. 33–47. [7] P. Baroni, D. Gabbay, M. Giacomin, L. van der Torre (Eds.), Handbook of Formal Argumentation,
College Publications, London, England, 2018. [8] A. Bochman, F. Cerutti, T. Rienstra, Causation and argumentation, in: D. Gabbay, G. Kern-Isberner, G. Simari, M. Thimm (Eds.), Handbook of Formal Argumentation, volume 3, College Pubblications, 2025.