# Unexplainability and Incomprehensibility of Artificial Intelligence

arXiv: https://arxiv.org/abs/1907.03869 · HTML: https://mlsanity.com/paper/1907.03869/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2019.

## Built on by (most cited first, 5 of 5)

- [A Review of Deep Learning Approaches for Non-Invasive Cognitive Impairment Detection](https://mlsanity.com/paper/2410.19898/) (2024)
- [Mask Usage Recognition using Vision Transformer with Transfer Learning and Data Augmentation](https://mlsanity.com/paper/2203.11542/) (2022)
- [Energy-frugal and Interpretable AI Hardware Design using Learning Automata](https://mlsanity.com/paper/2305.11928/) (2023)
- [Interpretable and Explainable Surrogate Modeling for Simulations: A State-of-the-Art Survey and Perspectives on Explainable AI for Decision-Making](https://mlsanity.com/paper/2604.14240/) (2026)
- [Actionable AI: Enabling Non Experts to Understand and Configure AI Systems](https://mlsanity.com/paper/2503.06803/) (2025)

## Abstract (arXiv)

Explainability and comprehensibility of AI are important requirements for intelligent systems deployed in real-world domains. Users want and frequently need to understand how decisions impacting them are made. Similarly it is important to understand how an intelligent system functions for safety and security reasons. In this paper, we describe two complementary impossibility results (Unexplainability and Incomprehensibility), essentially showing that advanced AIs would not be able to accurately explain some of their decisions and for the decisions they could explain people would not understand some of those explanations.