Embedding Fairness in AI Development: Introducing the Fair AI Scrum Handbook
- WAI CONTENT TEAM
- Dec 8, 2025
- 1 min read
Building fair and responsible AI requires more than algorithms; it demands structured, continuous reflection at every stage of development. The DIVERSIFAIR project’s Fair AI Scrum Handbook offers AI teams a practical guide to embedding fairness into Scrum workflows.
The handbook introduces five pillars of Fair AI Scrum and provides actionable tools and prompts to integrate fairness across:
Sprint Planning: Make fairness a deliberate part of your backlog.
Retrospectives: Reflect on fairness and AI impacts in production.
Roles, Events, and Artefacts: Foster inclusive decision-making throughout Scrum.
Designed for teams developing AI of any risk level, this handbook helps align AI development with ethical and responsible practices, ensuring your AI systems consider real-world impact and inclusivity.
📥 Find out more & to download the handbook: https://diversifair-project.eu/courses/fair-ai-scrum-handbook/?utm_source=womeninai&utm_medium=social&utm_campaign=deliverables
ABOUT DIVERSIFAIR
DIVERSIFAIR is an Erasmus+ project that aims to foster intersectional fairness in AI systems by creating educational resources, research, and policy guidance. Women in AI is proud to be a partner in this initiative, working alongside leading academic institutions, AI educators, and civil society organisations to build a more inclusive and ethical future for AI.
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Funded by the European Union. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the Culture Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.
This is such a timely resource — fairness often gets treated as an afterthought in AI dev, so having concrete prompts baked into sprint ceremonies makes total sense. The retrospective angle especially resonates with me, since that's usually where the real "wait, what did we actually build here?" conversations happen. Speaking of visual quality, it's funny how we obsess over making AI outputs look polished but rarely apply that same scrutiny to the ethical side — reminds me of how there's even a whole bad quality image maker tool out there for intentionally degrading images, which feels like the opposite problem. Anyway, definitely downloading this handbook for our next sprint cycle.
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