# Data Justice in Practice: A Guide for Developers

arXiv: https://arxiv.org/abs/2205.01037 · HTML: https://mlsanity.com/paper/2205.01037/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 12 Apr 2022.

## Builds on (most cited first, 3 of 3)

- [Understanding artificial intelligence ethics and safety](https://mlsanity.com/paper/1906.05684/) (2019)
- [Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges](https://mlsanity.com/paper/1905.04223/) (2019)
- [Ethical Assurance: A practical approach to the responsible design, development, and deployment of data-driven technologies](https://mlsanity.com/paper/2110.05164/) (2021)

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

- [Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in Education](https://mlsanity.com/paper/2311.05792/) (2023)
- [Don't "research fast and break things": On the ethics of Computational Social Science](https://mlsanity.com/paper/2206.06370/) (2022)

## Abstract (arXiv)

The Advancing Data Justice Research and Practice project aims to broaden understanding of the social, historical, cultural, political, and economic forces that contribute to discrimination and inequity in contemporary ecologies of data collection, governance, and use. This is the consultation draft of a guide for developers and organisations, which are producing, procuring, or using data-intensive technologies.In the first section, we introduce the field of data justice, from its early discussions to more recent proposals to relocate understandings of what data justice means. This section includes a description of the six pillars of data justice around which this guidance revolves. Next, to support developers in designing, developing, and deploying responsible and equitable data-intensive and AI/ML systems, we outline the AI/ML project lifecycle through a sociotechnical lens. To support the operationalisation data justice throughout the entirety of the AI/ML lifecycle and within data innovation ecosystems, we then present five overarching principles of responsible, equitable, and trustworthy data research and innovation practices, the SAFE-D principles-Safety, Accountability, Fairness, Explainability, and Data Quality, Integrity, Protection, and Privacy. The final section presents guiding questions that will help developers both address data justice issues throughout the AI/ML lifecycle and engage in reflective innovation practices that ensure the design, development, and deployment of responsible and equitable data-intensive and AI/ML systems.