# EUCA: the End-User-Centered Explainable AI Framework

arXiv: https://arxiv.org/abs/2102.02437 · HTML: https://mlsanity.com/paper/2102.02437/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2021.

## Built on by (most cited first, 10 of 10)

- [FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare](https://mlsanity.com/paper/2309.12325/) (2023)
- [Guidelines and Evaluation of Clinical Explainable AI in Medical Image Analysis](https://mlsanity.com/paper/2202.10553/) (2022)
- [Evaluating Explainable AI on a Multi-Modal Medical Imaging Task: Can Existing Algorithms Fulfill Clinical Requirements?](https://mlsanity.com/paper/2203.06487/) (2022)
- [LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception Tasks](https://mlsanity.com/paper/2402.12525/) (2024)
- [Why is plausibility surprisingly problematic as an XAI criterion?](https://mlsanity.com/paper/2303.17707/) (2023)
- [Fake News Detection After LLM Laundering: Measurement and Explanation](https://mlsanity.com/paper/2501.18649/) (2025)
- [Transcending XAI Algorithm Boundaries through End-User-Inspired Design](https://mlsanity.com/paper/2208.08739/) (2022)
- [Food Recognition and Nutritional Apps](https://mlsanity.com/paper/2307.05372/) (2023)
- [A survey and taxonomy of methods interpreting random forest models](https://mlsanity.com/paper/2407.12759/) (2024)
- [UCFE: A User-Centric Financial Expertise Benchmark for Large Language Models](https://mlsanity.com/paper/2410.14059/) (2024)

## Abstract (arXiv)

The ability to explain decisions to end-users is a necessity to deploy AI as critical decision support. Yet making AI explainable to non-technical end-users is a relatively ignored and challenging problem. To bridge the gap, we first identify twelve end-user-friendly explanatory forms that do not require technical knowledge to comprehend, including feature-, example-, and rule-based explanations. We then instantiate the explanatory forms as prototyping cards in four AI-assisted critical decision-making tasks, and conduct a user study to co-design low-fidelity prototypes with 32 layperson participants. The results confirm the relevance of using explanatory forms as building blocks of explanations, and identify their proprieties - pros, cons, applicable explanation goals, and design implications. The explanatory forms, their proprieties, and prototyping supports (including a suggested prototyping process, design templates and exemplars, and associated algorithms to actualize explanatory forms) constitute the End-User-Centered explainable AI framework EUCA, and is available at http://weinajin.github.io/end-user-xai . It serves as a practical prototyping toolkit for HCI/AI practitioners and researchers to understand user requirements and build end-user-centered explainable AI.