Explainable AI for trustworthy image analysis

Jordan E. Turley; Jeffrey A. Dunne; Zerotti Woods

doi:10.1117/12.2664001

12 June 2023 Explainable AI for trustworthy image analysis

Jordan E. Turley, Jeffrey A. Dunne, Zerotti Woods

Proceedings Volume 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V; 1253809 (2023) https://doi.org/10.1117/12.2664001
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

The capabilities of convolutional neural networks, and in fact all manner of artificial intelligence and machine learning capabilities, to explore data in various fields has been documented extensively throughout the literature. One common challenge with adopting AI/ML solutions, however, is the issue of trust. Decision makers are rightfully hesitant to take action based solely on “the computer said so” even if the computer has great confidence that it is correct. There is obvious value in a system that can answer the question of why it made a given prediction and back this up with specific evidence. Basic models like regression or nearest neighbors can support such answers but have significant limitations in real-world applications, and more capable models like neural networks are much too complex to interpret. We have developed a prototype system that combines convolutional neural networks with semantic representations of reasonableness. We use logic similar to how humans justify conclusions, breaking objects into smaller pieces that we trust a neural network to identify. Leveraging a suite of machine learning algorithms, the tool provides not merely an output “conclusion”, but a supporting string of evidence that humans can use to better understand the conclusion, as well as explore potential weaknesses in the AI/ML components (whether as a result of lack of sufficient training data, adversarial attempts to corrupt the system, etc.). We have applied this system to problems of object detection and semantic segmentation of images. This paper will provide an in-depth overview of the prototype and show some exemplar results.

Conference Presentation

Citation Download Citation

Jordan E. Turley, Jeffrey A. Dunne, and Zerotti Woods "Explainable AI for trustworthy image analysis", Proc. SPIE 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V, 1253809 (12 June 2023); https://doi.org/10.1117/12.2664001

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available