# What Does Explainable AI Really Mean? A New Conceptualization of Perspectives

arXiv: https://arxiv.org/abs/1710.00794 · HTML: https://mlsanity.com/paper/1710.00794/

## Key facts

- Cited by 72 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2017.

## Built on by (most cited first, 30 of 72)

- [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)
- [Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey](https://mlsanity.com/paper/2104.00950/) (2021)
- [On scientific understanding with artificial intelligence](https://mlsanity.com/paper/2204.01467/) (2022)
- [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)
- [Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications](https://mlsanity.com/paper/2103.04244/) (2021)
- [OpenForensics: Large-Scale Challenging Dataset For Multi-Face Forgery Detection And Segmentation In-The-Wild](https://mlsanity.com/paper/2107.14480/) (2021)
- [Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks](https://mlsanity.com/paper/2107.11400/) (2021)
- [Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction](https://mlsanity.com/paper/2409.00265/) (2024)
- [To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods](https://mlsanity.com/paper/2106.00461/) (2021)
- [`It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Values](https://mlsanity.com/paper/2307.10221/) (2023)
- [Explainable Machine Learning with Prior Knowledge: An Overview](https://mlsanity.com/paper/2105.10172/) (2021)
- [Interpretable Machine Learning in Physics: A Review](https://mlsanity.com/paper/2503.23616/) (2025)
- [DiCE4EL: Interpreting Process Predictions using a Milestone-Aware Counterfactual Approach](https://mlsanity.com/paper/2107.08697/) (2021)
- [Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classification](https://mlsanity.com/paper/2209.14974/) (2022)
- [Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications](https://mlsanity.com/paper/2203.10794/) (2022)
- [Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions](https://mlsanity.com/paper/2307.13149/) (2023)
- [Development and testing of an image transformer for explainable autonomous driving systems](https://mlsanity.com/paper/2110.05559/) (2021)
- [XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine Learning](https://mlsanity.com/paper/2202.11954/) (2022)
- [Comprehensible Artificial Intelligence on Knowledge Graphs: A survey](https://mlsanity.com/paper/2404.03499/) (2024)
- [Evaluating Cultural Awareness of LLMs for Yoruba, Malayalam, and English](https://mlsanity.com/paper/2410.01811/) (2024)
- [Explainable AI and Adoption of Financial Algorithmic Advisors: an Experimental Study](https://mlsanity.com/paper/2101.02555/) (2021)
- [AdViCE: Aggregated Visual Counterfactual Explanations for Machine Learning Model Validation](https://mlsanity.com/paper/2109.05629/) (2021)
- [Neuro-Symbolic AI for Military Applications](https://mlsanity.com/paper/2408.09224/) (2024)
- [Explainable Artificial Intelligence (XAI): An Engineering Perspective](https://mlsanity.com/paper/2101.03613/) (2021)
- [Trade-off Between Efficiency and Consistency for Removal-based Explanations](https://mlsanity.com/paper/2210.17426/) (2022)
- [On the Value of Labeled Data and Symbolic Methods for Hidden Neuron Activation Analysis](https://mlsanity.com/paper/2404.13567/) (2024)
- [Users are the North Star for AI Transparency](https://mlsanity.com/paper/2303.05500/) (2023)
- [Explainable AI applications in the Medical Domain: a systematic review](https://mlsanity.com/paper/2308.05411/) (2023)
- [Deep Quantum Graph Dreaming: Deciphering Neural Network Insights into Quantum Experiments](https://mlsanity.com/paper/2309.07056/) (2023)
- [A Comparative Analysis of Counterfactual Explanation Methods for Text Classifiers](https://mlsanity.com/paper/2411.02643/) (2024)

## Abstract (arXiv)

We characterize three notions of explainable AI that cut across research fields: opaque systems that offer no insight into its algo- rithmic mechanisms; interpretable systems where users can mathemat- ically analyze its algorithmic mechanisms; and comprehensible systems that emit symbols enabling user-driven explanations of how a conclusion is reached. The paper is motivated by a corpus analysis of NIPS, ACL, COGSCI, and ICCV/ECCV paper titles showing differences in how work on explainable AI is positioned in various fields. We close by introducing a fourth notion: truly explainable systems, where automated reasoning is central to output crafted explanations without requiring human post processing as final step of the generative process.