mlsanityData to 14 Oct 2026

Scope and Sense of Explainability for AI-Systems

arXiv 2112.10551 ↗First posted 20 Dec 2021cs.LG cs.AI cs.CYcited by 2

Not on the map. Why?

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.

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

14 of 39 references.

References · 14

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  2. 2017One pixel attack for fooling deep neural networkscited by 582
  3. 2020Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Surveycited by 254
  4. 2017Interpretable Convolutional Neural Networkscited by 162
  5. 2020Explainable deep learning models in medical image analysiscited by 111
  6. 2019Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Surveycited by 93
  7. 2020Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learningcited by 61
  8. 2019Model-free prediction of spatiotemporal dynamical systems with recurrent neural networks: Role of network spectral radiuscited by 34
  9. 2019Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based roboticscited by 21
  10. 2015Continuous online sequence learning with an unsupervised neural network modelcited by 18
  11. 2018Interpreting recurrent neural networks behaviour via excitable network attractorscited by 15
  12. 2019Echo State Networks with Self-Normalizing Activations on the Hyper-Spherecited by 5
2 more
  1. 2019Physics-Informed Echo State Networks for Chaotic Systems Forecastingcited by 5
  2. 2019Leveling the Playing Field -- Fairness in AI Versus Human Game Benchmarkscited by 4

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 1.

2 papers.

  1. 2023The role of causality in explainable artificial intelligencecited by 17
  2. 2024A Scale-Invariant Diagnostic Approach Towards Understanding Dynamics of Deep Neural Networkscited by 0