# On Explainability in AI-Solutions: A Cross-Domain Survey

arXiv: https://arxiv.org/abs/2210.05173 · HTML: https://mlsanity.com/paper/2210.05173/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 68 references that are in the map.
- First posted on arXiv 11 Oct 2022.

## Builds on (most cited first, 22 of 22)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI](https://mlsanity.com/paper/1907.07374/) (2019)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [Explainable Machine Learning in Deployment](https://mlsanity.com/paper/1909.06342/) (2019)
- [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018)
- [Explainable Machine Learning for Scientific Insights and Discoveries](https://mlsanity.com/paper/1905.08883/) (2019)
- [One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques](https://mlsanity.com/paper/1909.03012/) (2019)
- [What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use](https://mlsanity.com/paper/1905.05134/) (2019)
- [What do we need to build explainable AI systems for the medical domain?](https://mlsanity.com/paper/1712.09923/) (2017)
- [Expanding Explainability: Towards Social Transparency in AI systems](https://mlsanity.com/paper/2101.04719/) (2021)
- [Explainable deep learning models in medical image analysis](https://mlsanity.com/paper/2005.13799/) (2020)
- [Principles and Practice of Explainable Machine Learning](https://mlsanity.com/paper/2009.11698/) (2020)
- [AI in Finance: Challenges, Techniques and Opportunities](https://mlsanity.com/paper/2107.09051/) (2021)
- [Reliable and Explainable Machine Learning Methods for Accelerated Material Discovery](https://mlsanity.com/paper/1901.02717/) (2019)
- [Towards Explainable AI Planning as a Service](https://mlsanity.com/paper/1908.05059/) (2019)
- [Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach](https://mlsanity.com/paper/2003.07703/) (2020)
- [Knowledge-aware Autoencoders for Explainable Recommender Sytems](https://mlsanity.com/paper/1807.06300/) (2018)
- [Anomaly-based Intrusion Detection in Industrial Data with SVM and Random Forests](https://mlsanity.com/paper/1907.10374/) (2019)
- [Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development](https://mlsanity.com/paper/2011.08090/) (2020)
- [Evaluation of Machine Learning-based Anomaly Detection Algorithms on an Industrial Modbus/TCP Data Set](https://mlsanity.com/paper/1905.11757/) (2019)
- [Transfer Learning versus Multi-agent Learning regarding Distributed Decision-Making in Highway Traffic](https://mlsanity.com/paper/1810.08515/) (2018)

## Built on by (most cited first, 2 of 2)

- [Biological and Radiological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Prostate Cancer; Dictionary Version PM1.0](https://mlsanity.com/paper/2412.10967/) (2024)
- [Explainability Through Systematicity: The Hard Systematicity Challenge for Artificial Intelligence](https://mlsanity.com/paper/2507.22197/) (2025)

## Abstract (arXiv)

Artificial Intelligence (AI) increasingly shows its potential to outperform predicate logic algorithms and human control alike. In automatically deriving a system model, AI algorithms learn relations in data that are not detectable for humans. This great strength, however, also makes use of AI methods dubious. The more complex a model, the more difficult it is for a human to understand the reasoning for the decisions. As currently, fully automated AI algorithms are sparse, every algorithm has to provide a reasoning for human operators. For data engineers, metrics such as accuracy and sensitivity are sufficient. However, if models are interacting with non-experts, explanations have to be understandable. This work provides an extensive survey of literature on this topic, which, to a large part, consists of other surveys. The findings are mapped to ways of explaining decisions and reasons for explaining decisions. It shows that the heterogeneity of reasons and methods of and for explainability lead to individual explanatory frameworks.