# Towards Feminist Intersectional XAI: From Explainability to Response-Ability

arXiv: https://arxiv.org/abs/2305.03375 · HTML: https://mlsanity.com/paper/2305.03375/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 57 references that are in the map.
- First posted on arXiv 5 May 2023.

## Builds on (most cited first, 12 of 12)

- [An Intersectional Definition of Fairness](https://mlsanity.com/paper/1807.08362/) (2018)
- [Towards a Critical Race Methodology in Algorithmic Fairness](https://mlsanity.com/paper/1912.03593/) (2019)
- [Expanding Explainability: Towards Social Transparency in AI systems](https://mlsanity.com/paper/2101.04719/) (2021)
- [Participation is not a Design Fix for Machine Learning](https://mlsanity.com/paper/2007.02423/) (2020)
- [Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence](https://mlsanity.com/paper/2007.04068/) (2020)
- [Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach](https://mlsanity.com/paper/2002.01092/) (2020)
- [The Who in XAI: How AI Background Shapes Perceptions of AI Explanations](https://mlsanity.com/paper/2107.13509/) (2021)
- [Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values](https://mlsanity.com/paper/2206.15465/) (2022)
- [Epistemic values in feature importance methods: Lessons from feminist epistemology](https://mlsanity.com/paper/2101.12737/) (2021)
- [Grounding Explainability Within the Context of Global South in XAI](https://mlsanity.com/paper/2205.06919/) (2022)
- [Situated Case Studies for a Human-Centered Design of Explanation User Interfaces](https://mlsanity.com/paper/2103.15462/) (2021)
- [LEx: A Framework for Operationalising Layers of Machine Learning Explanations](https://mlsanity.com/paper/2104.09612/) (2021)

## 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)

This paper follows calls for critical approaches to computing and conceptualisations of intersectional, feminist, decolonial HCI and AI design and asks what a feminist intersectional perspective in HCXAI research and design might look like. Sketching out initial research directions and implications for explainable AI design, it suggests that explainability from a feminist perspective would include the fostering of response-ability - the capacity to critically evaluate and respond to AI systems - and would centre marginalised perspectives.