DeepAI AI Chat
Log In Sign Up

Machine Reasoning Explainability

by   Kristijonas Čyras, et al.

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.


page 1

page 2

page 3

page 4


The Need for Standardized Explainability

Explainable AI (XAI) is paramount in industry-grade AI; however existing...

Expanding Explainability: From Explainable Artificial Intelligence to Explainable Hardware

The increasing opaqueness of AI and its growing influence on our digital...

Explainable Deep RDFS Reasoner

Recent research efforts aiming to bridge the Neural-Symbolic gap for RDF...

Towards Feminist Intersectional XAI: From Explainability to Response-Ability

This paper follows calls for critical approaches to computing and concep...

Utterance Classification with Logical Neural Network: Explainable AI for Mental Disorder Diagnosis

In response to the global challenge of mental health problems, we propos...

Algorithmic Governance for Explainability: A Comparative Overview of Progress and Trends

The explainability of AI has transformed from a purely technical issue t...

Explainable Agents Through Social Cues: A Review

How to provide explanations has experienced a surge of interest in Human...