# Foundations of Explainable Knowledge-Enabled Systems

arXiv: https://arxiv.org/abs/2003.07520 · HTML: https://mlsanity.com/paper/2003.07520/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2020.

## Built on by (most cited first, 6 of 6)

- [GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks](https://mlsanity.com/paper/2107.11889/) (2021)
- [Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes](https://mlsanity.com/paper/2302.05752/) (2023)
- ["Explanation" is Not a Technical Term: The Problem of Ambiguity in XAI](https://mlsanity.com/paper/2207.00007/) (2022)
- [Combining Sub-Symbolic and Symbolic Methods for Explainability](https://mlsanity.com/paper/2112.01844/) (2021)
- [On the Multiple Roles of Ontologies in Explainable AI](https://mlsanity.com/paper/2311.04778/) (2023)
- [Re-imagining health and well-being in low resource African settings using an augmented AI system and a 3D digital twin](https://mlsanity.com/paper/2306.01772/) (2023)

## Abstract (arXiv)

Explainability has been an important goal since the early days of Artificial Intelligence. Several approaches for producing explanations have been developed. However, many of these approaches were tightly coupled with the capabilities of the artificial intelligence systems at the time. With the proliferation of AI-enabled systems in sometimes critical settings, there is a need for them to be explainable to end-users and decision-makers. We present a historical overview of explainable artificial intelligence systems, with a focus on knowledge-enabled systems, spanning the expert systems, cognitive assistants, semantic applications, and machine learning domains. Additionally, borrowing from the strengths of past approaches and identifying gaps needed to make explanations user- and context-focused, we propose new definitions for explanations and explainable knowledge-enabled systems.