# Machine Reasoning Explainability

arXiv: https://arxiv.org/abs/2009.00418 · HTML: https://mlsanity.com/paper/2009.00418/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [A Relational Tsetlin Machine with Applications to Natural Language Understanding](https://mlsanity.com/paper/2102.10952/) (2021)
- [Explainable AI applications in the Medical Domain: a systematic review](https://mlsanity.com/paper/2308.05411/) (2023)
- [Finding Counterfactual Explanations through Constraint Relaxations](https://mlsanity.com/paper/2204.03429/) (2022)
- [Trustworthy and Explainable Decision-Making for Workforce allocation](https://mlsanity.com/paper/2412.10272/) (2024)

## Abstract (arXiv)

As a field of AI, Machine Reasoning (MR) uses largely symbolic means to formalize and emulate abstract reasoning. Studies in early MR have notably started inquiries into Explainable AI (XAI) -- arguably one of the biggest concerns today for the AI community. Work on explainable MR as well as on MR approaches to explainability in other areas of AI has continued ever since. It is especially potent in modern MR branches, such as argumentation, constraint and logic programming, planning. We hereby aim to provide a selective overview of MR explainability techniques and studies in hopes that insights from this long track of research will complement well the current XAI landscape. This document reports our work in-progress on MR explainability.