# Roadmap of Designing Cognitive Metrics for Explainable Artificial Intelligence (XAI)

arXiv: https://arxiv.org/abs/2108.01737 · HTML: https://mlsanity.com/paper/2108.01737/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2021.

## Built on by (most cited first, 13 of 13)

- [Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation](https://mlsanity.com/paper/2305.02231/) (2023)
- [Explain Any Concept: Segment Anything Meets Concept-Based Explanation](https://mlsanity.com/paper/2305.10289/) (2023)
- [Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI Assistant](https://mlsanity.com/paper/2501.17546/) (2025)
- [XCoOp: Explainable Prompt Learning for Computer-Aided Diagnosis via Concept-guided Context Optimization](https://mlsanity.com/paper/2403.09410/) (2024)
- [MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept Alignment](https://mlsanity.com/paper/2401.08527/) (2024)
- [Impact Of Explainable AI On Cognitive Load: Insights From An Empirical Study](https://mlsanity.com/paper/2304.08861/) (2023)
- [Resisting Out-of-Distribution Data Problem in Perturbation of XAI](https://mlsanity.com/paper/2107.14000/) (2021)
- [Motion2Meaning: A Clinician-Centered Framework for Contestable LLM in Parkinson's Disease Gait Interpretation](https://mlsanity.com/paper/2512.08934/) (2025)
- [Towards a Comprehensive Human-Centred Evaluation Framework for Explainable AI](https://mlsanity.com/paper/2308.06274/) (2023)
- [Enriching Artificial Intelligence Explanations with Knowledge Fragments](https://mlsanity.com/paper/2204.05579/) (2022)
- [Toward enriched Cognitive Learning with XAI](https://mlsanity.com/paper/2312.12290/) (2023)
- [Improving the Explain-Any-Concept by Introducing Nonlinearity to the Trainable Surrogate Model](https://mlsanity.com/paper/2405.11837/) (2024)
- [CoPA: Hierarchical Concept Prompting and Aggregating Network for Explainable Diagnosis](https://mlsanity.com/paper/2510.03767/) (2025)

## Abstract (arXiv)

More recently, Explainable Artificial Intelligence (XAI) research has shifted to focus on a more pragmatic or naturalistic account of understanding, that is, whether the stakeholders understand the explanation. This point is especially important for research on evaluation methods for XAI systems. Thus, another direction where XAI research can benefit significantly from cognitive science and psychology research is ways to measure understanding of users, responses and attitudes. These measures can be used to quantify explanation quality and as feedback to the XAI system to improve the explanations. The current report aims to propose suitable metrics for evaluating XAI systems from the perspective of the cognitive states and processes of stakeholders. We elaborate on 7 dimensions, i.e., goodness, satisfaction, user understanding, curiosity & engagement, trust & reliance, controllability & interactivity, and learning curve & productivity, together with the recommended subjective and objective psychological measures. We then provide more details about how we can use the recommended measures to evaluate a visual classification XAI system according to the recommended cognitive metrics.