# Scope and Sense of Explainability for AI-Systems

arXiv: https://arxiv.org/abs/2112.10551 · HTML: https://mlsanity.com/paper/2112.10551/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 20 Dec 2021.

## Builds on (most cited first, 14 of 14)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [One pixel attack for fooling deep neural networks](https://mlsanity.com/paper/1710.08864/) (2017)
- [Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey](https://mlsanity.com/paper/2006.11371/) (2020)
- [Interpretable Convolutional Neural Networks](https://mlsanity.com/paper/1710.00935/) (2017)
- [Explainable deep learning models in medical image analysis](https://mlsanity.com/paper/2005.13799/) (2020)
- [Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey](https://mlsanity.com/paper/1911.12116/) (2019)
- [Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning](https://mlsanity.com/paper/2001.09684/) (2020)
- [Model-free prediction of spatiotemporal dynamical systems with recurrent neural networks: Role of network spectral radius](https://mlsanity.com/paper/1910.04426/) (2019)
- [Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics](https://mlsanity.com/paper/1901.08651/) (2019)
- [Continuous online sequence learning with an unsupervised neural network model](https://mlsanity.com/paper/1512.05463/) (2015)
- [Interpreting recurrent neural networks behaviour via excitable network attractors](https://mlsanity.com/paper/1807.10478/) (2018)
- [Echo State Networks with Self-Normalizing Activations on the Hyper-Sphere](https://mlsanity.com/paper/1903.11691/) (2019)
- [Physics-Informed Echo State Networks for Chaotic Systems Forecasting](https://mlsanity.com/paper/1906.11122/) (2019)
- [Leveling the Playing Field -- Fairness in AI Versus Human Game Benchmarks](https://mlsanity.com/paper/1903.07008/) (2019)

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

- [The role of causality in explainable artificial intelligence](https://mlsanity.com/paper/2309.09901/) (2023)
- [A Scale-Invariant Diagnostic Approach Towards Understanding Dynamics of Deep Neural Networks](https://mlsanity.com/paper/2407.09585/) (2024)

## Abstract (arXiv)

Certain aspects of the explainability of AI systems will be critically discussed. This especially with focus on the feasibility of the task of making every AI system explainable. Emphasis will be given to difficulties related to the explainability of highly complex and efficient AI systems which deliver decisions whose explanation defies classical logical schemes of cause and effect. AI systems have provably delivered unintelligible solutions which in retrospect were characterized as ingenious (for example move 37 of the game 2 of AlphaGo). It will be elaborated on arguments supporting the notion that if AI-solutions were to be discarded in advance because of their not being thoroughly comprehensible, a great deal of the potentiality of intelligent systems would be wasted.