# Explainable Artificial Intelligence and Machine Learning: A reality rooted perspective

arXiv: https://arxiv.org/abs/2001.09464 · HTML: https://mlsanity.com/paper/2001.09464/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2020.

## Built on by (most cited first, 12 of 12)

- [Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective](https://mlsanity.com/paper/2304.06412/) (2023)
- [Shapley variable importance clouds for interpretable machine learning](https://mlsanity.com/paper/2110.02484/) (2021)
- [TorchEsegeta: Framework for Interpretability and Explainability of Image-based Deep Learning Models](https://mlsanity.com/paper/2110.08429/) (2021)
- [Enhancing Breast Cancer Diagnosis in Mammography: Evaluation and Integration of Convolutional Neural Networks and Explainable AI](https://mlsanity.com/paper/2404.03892/) (2024)
- [Explainable automatic industrial carbon footprint estimation from bank transaction classification using natural language processing](https://mlsanity.com/paper/2405.14505/) (2024)
- [Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions](https://mlsanity.com/paper/2211.01036/) (2022)
- [An Efficient Ensemble Explainable AI (XAI) Approach for Morphed Face Detection](https://mlsanity.com/paper/2304.14509/) (2023)
- [Beyond Single-Feature Importance with ICECREAM](https://mlsanity.com/paper/2307.09779/) (2023)
- [Self-Admitted Technical Debt Detection Approaches: A Decade Systematic Review](https://mlsanity.com/paper/2312.15020/) (2023)
- [A survey and taxonomy of methods interpreting random forest models](https://mlsanity.com/paper/2407.12759/) (2024)
- [Enriching Artificial Intelligence Explanations with Knowledge Fragments](https://mlsanity.com/paper/2204.05579/) (2022)
- [On Explaining Proxy Discrimination and Unfairness in Individual Decisions Made by AI Systems](https://mlsanity.com/paper/2509.25662/) (2025)

## Abstract (arXiv)

We are used to the availability of big data generated in nearly all fields of science as a consequence of technological progress. However, the analysis of such data possess vast challenges. One of these relates to the explainability of artificial intelligence (AI) or machine learning methods. Currently, many of such methods are non-transparent with respect to their working mechanism and for this reason are called black box models, most notably deep learning methods. However, it has been realized that this constitutes severe problems for a number of fields including the health sciences and criminal justice and arguments have been brought forward in favor of an explainable AI. In this paper, we do not assume the usual perspective presenting explainable AI as it should be, but rather we provide a discussion what explainable AI can be. The difference is that we do not present wishful thinking but reality grounded properties in relation to a scientific theory beyond physics.