Asking ‘Why’ in AI: Explainability of intelligent systems – perspectives and challenges

Abstract

Recent rapid progress in machine learning (ML), particularly so‐called ‘deep learning’, has led to a resurgence in interest in explainability of artificial intelligence (AI) systems, reviving an area of research dating back to the 1970s. The aim of this article is to view current issues concerning ML‐based AI systems from the perspective of classical AI, showing that the fundamental problems are far from new, and arguing that elements of that earlier work offer routes to making progress towards explainable AI today.

Document Details

Document Type
Pub Defense Publication
Publication Date
Apr 01, 2018
Source ID
10.1002/isaf.1422

Entities

People

  • Alun Preece

Organizations

  • Cardiff University
  • United States Army Research Laboratory

Tags

Readers

  • East Asian Political and Security Studies within the Soviet Union
  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Neural Networks