A Preliminary Look at Heuristic Analysis for Assessing Artificial Intelligence Explainability

Abstract

Artificial Intelligence and Machine Learning (AI/ML) models are increasingly criticized for their “black-box” nature. Therefore, eXplainable AI (XAI) approaches to extract human-interpretable decision processes from algorithms have been explored. However, XAI research lacks understanding of algorithmic explainability from a human factors’ perspective. This paper presents a repeatable human factors heuristic analysis for XAI with a demonstration on four decision tree classifier algorithms.

Document Details

• Document Type: Pub Defense Publication
• Publication Date: Jun 01, 2020
• Source ID: 10.37394/232018.2020.8.9

Entities

People

• Kara Combs
• Mary Fendley
• Trevor Bihl

Organizations

• Air Force Research Laboratory
• Wright State University

Tags

Fields of Study

• Computer science

Readers

• Neural Network Machine Learning.
• Systems Analysis and Design

Technology Areas

• AI & ML
• AI & ML - DoD AI Strategy
• AI & ML - Machine Learning Algorithms
• AI & ML - Neural Networks