Explainable AI for Decision Making
Publicerad
Författare
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This thesis examines the feasibility of using an AI system to support decision making
processes in identifying potential root causes of quality issues in industrial and
marine power systems. The AI system employs a Retrieval Augmented Generation
(RAG) architecture, utilizing Large Language Models (LLMs).
The research investigates whether pre-trained LLMs, combined with a constructed
database in the RAG framework, are sufficient to provide support in a highly specific
domain context. It also explores the factors that influence user acceptance and trust
in the AI system. The evaluation includes both quantitative metrics and qualitative
user tests with domain experts.
The project was conducted in collaboration with Volvo Penta, a power solution
provider, and all data collection and user testing were performed at the company.
The findings suggest that the system can effectively retrieve and summarize historical
data to aid in identifying the root causes of quality issues. Additionally, the study
reveals that user satisfaction and trust of AI-driven insights are primarily influenced
by the system’s ability to explain its reasoning process for reaching conclusions.
Beskrivning
Ämne/nyckelord
Generative AI, Large Language Models, Retrieval Augmented Generation, AI System, Explainable AI, Decision Support, Root Cause Analysis, Quality Issues