Grounding Explainability Within the Context of Global South in XAI
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Abstract (arXiv)
In this position paper, we propose building a broader and deeper understanding around Explainability in AI by 'grounding' it in social contexts, the socio-technical systems operate in. We situate our understanding of grounded explainability in the 'Global South' in general and India in particular and express the need for more research within the global south context when it comes to explainability and AI.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 4 of its 18 references that are in the map. First posted on arXiv 13 May 2022.
Builds on
4 of 18 references.
References · 4
- 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
- 2021Re-imagining Algorithmic Fairness in India and Beyondcited by 104
- 2020Human-centered Explainable AI: Towards a Reflective Sociotechnical Approachcited by 90
- 2019Measuring the Quality of Explanations: The System Causability Scale (SCS). Comparing Human and Machine Explanationscited by 64
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Citing papers per year: 2023: 2.
2 papers.