# AI Sustainability in Practice Part One: Foundations for Sustainable AI Projects

arXiv: https://arxiv.org/abs/2403.14635 · HTML: https://mlsanity.com/paper/2403.14635/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 55 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Builds on (most cited first, 2 of 2)

- [Conscientious Classification: A Data Scientist's Guide to Discrimination-Aware Classification](https://mlsanity.com/paper/1907.09013/) (2019)
- [Tackling COVID-19 through Responsible AI Innovation: Five Steps in the Right Direction](https://mlsanity.com/paper/2008.06755/) (2020)

## Built on by (most cited first, 1 of 1)

- [Making AI Evaluation Deployment Relevant Through Context Specification](https://mlsanity.com/paper/2603.06811/) (2026)

## Abstract (arXiv)

Sustainable AI projects are continuously responsive to the transformative effects as well as short-, medium-, and long-term impacts on individuals and society that the design, development, and deployment of AI technologies may have. Projects, which centre AI Sustainability, ensure that values-led, collaborative, and anticipatory reflection both guides the assessment of potential social and ethical impacts and steers responsible innovation practices. This workbook is the first part of a pair that provides the concepts and tools needed to put AI Sustainability into practice. It introduces the SUM Values, which help AI project teams to assess the potential societal impacts and ethical permissibility of their projects. It then presents a Stakeholder Engagement Process (SEP), which provides tools to facilitate proportionate engagement of and input from stakeholders with an emphasis on equitable and meaningful participation and positionality awareness.