# Data and System Perspectives of Sustainable Artificial Intelligence

arXiv: https://arxiv.org/abs/2501.07487 · HTML: https://mlsanity.com/paper/2501.07487/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 13 Jan 2025.

## Builds on (most cited first, 7 of 7)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [Log-based Anomaly Detection with Deep Learning: How Far Are We?](https://mlsanity.com/paper/2202.04301/) (2022)
- [In Defense of Core-set: A Density-aware Core-set Selection for Active Learning](https://mlsanity.com/paper/2206.04838/) (2022)
- [AlphaClean: Automatic Generation of Data Cleaning Pipelines](https://mlsanity.com/paper/1904.11827/) (2019)
- [Synthetic Dataset Generation for Privacy-Preserving Machine Learning](https://mlsanity.com/paper/2210.03205/) (2022)

## Built on by (most cited first, 2 of 2)

- [StoryBench: A Dynamic Benchmark for Evaluating Long-Term Memory with Multi Turns](https://mlsanity.com/paper/2506.13356/) (2025)
- [Artificial Intelligence in Open Source Software Engineering: A Foundation for Sustainability](https://mlsanity.com/paper/2602.07071/) (2026)

## Abstract (arXiv)

Sustainable AI is a subfield of AI for concerning developing and using AI systems in ways of aiming to reduce environmental impact and achieve sustainability. Sustainable AI is increasingly important given that training of and inference with AI models such as large langrage models are consuming a large amount of computing power. In this article, we discuss current issues, opportunities and example solutions for addressing these issues, and future challenges to tackle, from the data and system perspectives, related to data acquisition, data processing, and AI model training and inference.