# Grounding Explainability Within the Context of Global South in XAI

arXiv: https://arxiv.org/abs/2205.06919 · HTML: https://mlsanity.com/paper/2205.06919/

## 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 (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)
- [Re-imagining Algorithmic Fairness in India and Beyond](https://mlsanity.com/paper/2101.09995/) (2021)
- [Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach](https://mlsanity.com/paper/2002.01092/) (2020)
- [Measuring the Quality of Explanations: The System Causability Scale (SCS). Comparing Human and Machine Explanations](https://mlsanity.com/paper/1912.09024/) (2019)

## Built on by (most cited first, 2 of 2)

- [Towards Feminist Intersectional XAI: From Explainability to Response-Ability](https://mlsanity.com/paper/2305.03375/) (2023)
- [Context, Utility and Influence of an Explanation](https://mlsanity.com/paper/2303.13552/) (2023)

## 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.