# A User-Centred Framework for Explainable Artificial Intelligence in Human-Robot Interaction

arXiv: https://arxiv.org/abs/2109.12912 · HTML: https://mlsanity.com/paper/2109.12912/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 37 references that are in the map.
- First posted on arXiv 27 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017)
- [Expanding Explainability: Towards Social Transparency in AI systems](https://mlsanity.com/paper/2101.04719/) (2021)
- [Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences](https://mlsanity.com/paper/1712.00547/) (2017)
- [Explainable Artificial Intelligence: a Systematic Review](https://mlsanity.com/paper/2006.00093/) (2020)
- [One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency](https://mlsanity.com/paper/2001.09734/) (2020)

## Built on by (most cited first, 5 of 5)

- [How much informative is your XAI? A decision-making assessment task to objectively measure the goodness of explanations](https://mlsanity.com/paper/2312.04379/) (2023)
- [Mining multi-modal communication patterns in interaction with explainable and non-explainable robots](https://mlsanity.com/paper/2312.14634/) (2023)
- [AI-HRI Brings New Dimensions to Human-Aware Design for Human-Aware AI](https://mlsanity.com/paper/2210.11832/) (2022)
- [Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks](https://mlsanity.com/paper/2411.10176/) (2024)
- [A Human-Centric Evaluation of a Retrieval-Augmented Generation System for Explaining Quebec Insurance Contracts](https://mlsanity.com/paper/2607.15963/) (2026)

## Abstract (arXiv)

State of the art Artificial Intelligence (AI) techniques have reached an impressive complexity. Consequently, researchers are discovering more and more methods to use them in real-world applications. However, the complexity of such systems requires the introduction of methods that make those transparent to the human user. The AI community is trying to overcome the problem by introducing the Explainable AI (XAI) field, which is tentative to make AI algorithms less opaque. However, in recent years, it became clearer that XAI is much more than a computer science problem: since it is about communication, XAI is also a Human-Agent Interaction problem. Moreover, AI came out of the laboratories to be used in real life. This implies the need for XAI solutions tailored to non-expert users. Hence, we propose a user-centred framework for XAI that focuses on its social-interactive aspect taking inspiration from cognitive and social sciences' theories and findings. The framework aims to provide a structure for interactive XAI solutions thought for non-expert users.