Four Principles of Explainable Artificial Intelligence (Draft)

Published

August 18, 2020

Author(s)

P Phillips, Carina Hahn, Peter Fontana, David A. Broniatowski, Mark A. Przybocki

Abstract

We introduce four principles for explainable artificial intelligence (AI) that comprise the fundamental properties for explainable AI systems. They were developed to encompass the multidisciplinary nature of explainable AI, including the fields of computer science, engineering, and psychology. Because one size fits all explanations do not exist, different users will require different types of explanations. We present five categories of explanation and summarize theories of explainable AI. We give an overview of the algorithms in the field that cover the major classes of explainable algorithms. As a baseline comparison, we assess how well explanations provided by people follow our four principles. This assessment provides insights to the challenges of designing explainable AI systems.

Citation

NIST Interagency/Internal Report (NISTIR) - 8312-draft

Report Number

8312-draft

NIST Pub Series

NIST Interagency/Internal Report (NISTIR)

Pub Type

NIST Pubs

Download Paper

https://doi.org/10.6028/NIST.IR.8312-draft

Local Download

Keywords

Artificial Intelligence (AI), explainable AI, trustworthy AI

Citation

Phillips, P. , Hahn, C. , Fontana, P. , Broniatowski, D. and Przybocki, M. (2020), Four Principles of Explainable Artificial Intelligence (Draft), NIST Interagency/Internal Report (NISTIR), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.IR.8312-draft, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=930794 (Accessed November 20, 2024)