Abstract
With artificial intelligence (AI) becoming ubiquitous in a broad range of application domains, the opacity of deep learning models remains an obstacle to adaptation within safety-critical systems. Explainable AI (XAI) aims to build trust in AI systems by revealing important inner mechanisms of what has been treated as a black box by human users. This thesis specifically aims to improve the transparency and trustworthiness of deep learning algorithms by combining attribution methods with image segmentation methods. This thesis has the potential to improve the trust and acceptance of AI systems, leading to more responsible and ethical AI applications. An exploratory algorithm called ESAX is introduced and shows how performance greater than other top attribution methods on PIC testing can be achieved in some cases. These results lay a foundation for future work in segmentation attribution.
Thesis Completion
2023
Semester
Spring
Thesis Chair/Advisor
Ewetz, Rickard
Degree
Bachelor of Science in Electrical Engineering (B.S.E.E.)
College
College of Engineering and Computer Science
Department
Electrical and Computer Engineering
Degree Program
Electrical Engineering
Language
English
Access Status
Open Access
Release Date
11-15-2023
Recommended Citation
Rocks, Garrett J., "Towards Explainable AI Using Attribution Methods and Image Segmentation" (2023). Honors Undergraduate Theses. 1414.
https://stars.library.ucf.edu/honorstheses/1414