# Explaining the ghosts: Feminist intersectional XAI and cartography as methods to account for invisible labour

arXiv: https://arxiv.org/abs/2305.03376 · HTML: https://mlsanity.com/paper/2305.03376/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 38 references that are in the map.
- First posted on arXiv 5 May 2023.

## Builds on (most cited first, 4 of 4)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach](https://mlsanity.com/paper/2002.01092/) (2020)
- [Stakeholders in Explainable AI](https://mlsanity.com/paper/1810.00184/) (2018)
- [Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theory](https://mlsanity.com/paper/2205.05057/) (2022)

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

- [Emergent, not Immanent: A Baradian Reading of Explainable AI](https://mlsanity.com/paper/2601.15029/) (2026)

## Abstract (arXiv)

Contemporary automation through AI entails a substantial amount of behind-the-scenes human labour, which is often both invisibilised and underpaid. Since invisible labour, including labelling and maintenance work, is an integral part of contemporary AI systems, it remains important to sensitise users to its role. We suggest that this could be done through explainable AI (XAI) design, particularly feminist intersectional XAI. We propose the method of cartography, which stems from feminist intersectional research, to draw out a systemic perspective of AI and include dimensions of AI that pertain to invisible labour.