Balancing Accuracy and Efficiency in Distributed Machine Learning Systems: A Framework for Policy and Risk Management

DOI

10.2991/978-94-6463-738-0_56How to use a DOI?

Keywords

Accuracy-efficiency trade-off; distributed machine learning; regulatory oversight; uncertainty quantification; high-stakes applications; autonomous systems; technology policy

Abstract

The trade-off between accuracy and efficiency is a fundamental principle in computer science, shaping advancements in fields such as hardware design, image processing, and approximate computing. This trade-off is particularly significant in distributed machine learning (ML) systems, where optimizing computational performance while maintaining correctness is essential. High-stakes applications, including autonomous vehicles and military drones, underscore the critical risks and uncertainties arising from this balance, necessitating robust regulatory oversight. This paper explores the accuracy-efficiency spectrum across computing disciplines, emphasizing its implications in distributed ML systems. We argue for the development of tools by ML researchers to quantify and expose system uncertainties, enabling policymakers to better assess trade-offs, make informed decisions, and mitigate risks. By shifting the focus from low-level technical details to actionable insights, these tools can bridge the gap between technology and policy. We pro- pose a regulatory framework tailored for high-risk domains, addressing the challenges at the intersection of ML innovation and governance. This work provides a foundation for informed decision-making, fostering safer deployment of distributed ML systems in critical applications.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Prashanth Chevva
PY  - 2025
DA  - 2025/06/22
TI  - Balancing Accuracy and Efficiency in Distributed Machine Learning Systems: A Framework for Policy and Risk Management
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 702
EP  - 712
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_56
DO  - 10.2991/978-94-6463-738-0_56
ID  - Chevva2025
ER  -