# Explanation as a process: user-centric construction of multi-level and multi-modal explanations

arXiv: https://arxiv.org/abs/2110.03759 · HTML: https://mlsanity.com/paper/2110.03759/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 7 Oct 2021.

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

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research](https://mlsanity.com/paper/2102.07817/) (2021)
- [Grounding Visual Explanations](https://mlsanity.com/paper/1807.09685/) (2018)
- [The Pragmatic Turn in Explainable Artificial Intelligence (XAI)](https://mlsanity.com/paper/2002.09595/) (2020)
- [The Grammar of Interactive Explanatory Model Analysis](https://mlsanity.com/paper/2005.00497/) (2020)
- [Should We Trust (X)AI? Design Dimensions for Structured Experimental Evaluations](https://mlsanity.com/paper/2009.06433/) (2020)

## Built on by (most cited first, 4 of 4)

- [Leveraging Explanations in Interactive Machine Learning: An Overview](https://mlsanity.com/paper/2207.14526/) (2022)
- [Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication](https://mlsanity.com/paper/2302.03460/) (2023)
- [Explaining Hate Speech Classification with Model Agnostic Methods](https://mlsanity.com/paper/2306.00021/) (2023)
- [CorrLoss: Integrating Co-Occurrence Domain Knowledge for Affect Recognition](https://mlsanity.com/paper/2210.17233/) (2022)

## Abstract (arXiv)

In the last years, XAI research has mainly been concerned with developing new technical approaches to explain deep learning models. Just recent research has started to acknowledge the need to tailor explanations to different contexts and requirements of stakeholders. Explanations must not only suit developers of models, but also domain experts as well as end users. Thus, in order to satisfy different stakeholders, explanation methods need to be combined. While multi-modal explanations have been used to make model predictions more transparent, less research has focused on treating explanation as a process, where users can ask for information according to the level of understanding gained at a certain point in time. Consequently, an opportunity to explore explanations on different levels of abstraction should be provided besides multi-modal explanations. We present a process-based approach that combines multi-level and multi-modal explanations. The user can ask for textual explanations or visualizations through conversational interaction in a drill-down manner. We use Inductive Logic Programming, an interpretable machine learning approach, to learn a comprehensible model. Further, we present an algorithm that creates an explanatory tree for each example for which a classifier decision is to be explained. The explanatory tree can be navigated by the user to get answers of different levels of detail. We provide a proof-of-concept implementation for concepts induced from a semantic net about living beings.