mlsanityData to 7 Oct 2026

Towards Feminist Intersectional XAI: From Explainability to Response-Ability

arXiv 2305.03375 ↗First posted 5 May 2023cs.HC cs.AIcited by 1

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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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 12 of its 57 references that are in the map. First posted on arXiv 5 May 2023.

Builds on

12 of 57 references.

References · 12

  1. 2018An Intersectional Definition of Fairnesscited by 126
  2. 2019Towards a Critical Race Methodology in Algorithmic Fairnesscited by 124
  3. 2021Expanding Explainability: Towards Social Transparency in AI systemscited by 124
  4. 2020Participation is not a Design Fix for Machine Learningcited by 110
  5. 2020Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligencecited by 100
  6. 2020Human-centered Explainable AI: Towards a Reflective Sociotechnical Approachcited by 90
  7. 2021The Who in XAI: How AI Background Shapes Perceptions of AI Explanationscited by 48
  8. 2022Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Valuescited by 25
  9. 2021Epistemic values in feature importance methods: Lessons from feminist epistemologycited by 11
  10. 2022Grounding Explainability Within the Context of Global South in XAIcited by 2
  11. 2021Situated Case Studies for a Human-Centered Design of Explanation User Interfacescited by 1
  12. 2021LEx: A Framework for Operationalising Layers of Machine Learning Explanationscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Emergent, not Immanent: A Baradian Reading of Explainable AIcited by 0