Explainable AI and User Experience. Prototyping and Evaluating an UX-Optimized XAI Interface in Computer Vision

Inhaltsverzeichnis (Table of Contents)

• Introduction
• Theoretical Foundation
  • Artificial Intelligence (AI)
  • Explainable Artificial Intelligence (XAI)
    • Explainability and Related Terms
    • Definition
    • Taxonomy of XAI Methods
  • User Experience (UX)
• Methodology
  • Systematic Literature Research (SLR)
  • User-centered XAI Design
• Prototyping and Evaluating an XAI Interface
  • Deriving Principles of User Experience
    • UX Principles in general
    • UX Principles according to XAI
  • Prototyping an XAI Interface for Computer Vision Tasks
    • Phase 1: Context
    • Phase 2: User
    • Phase 3: Solution
  • Evaluating an XAI Interface
    • Construction of an XAI-related Questionnaire
      • User Experience Questionnaire (UEQ)
      • Adapting the UEQ according to XAI
    • User Study
      • Design and Execution
      • General Results
      • Results from Quantitative Data Analysis

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This thesis explores the impact of Explainable Artificial Intelligence (XAI) on User Experience (UX), specifically in the context of computer vision tasks. It aims to prototype and evaluate a UX-optimized XAI interface for brain tumor detection using a Convolutional Neural Network (CNN) and the Local Interpretable Model-agnostic Explanations (LIME) method. This research investigates the influence of visual explanations on user trust, acceptance, and overall UX, considering both pragmatic and hedonic qualities.

• The role of XAI in enhancing user trust and acceptance of AI-based decision support systems.
• The integration of UX principles in the design and evaluation of XAI interfaces.
• The impact of visual explanations on the usability and user experience of XAI systems.
• The development of a UX-optimized XAI interface for brain tumor detection in radiology.
• The quantification of UX dimensions using the User Experience Questionnaire (UEQ) and its adaptation for XAI.

Zusammenfassung der Kapitel (Chapter Summaries)

• Introduction: This chapter sets the scene by discussing the increasing relevance of AI and the need for explainability in AI-based systems. The author highlights the importance of user-centered design and UX principles in the development of XAI systems.
• Theoretical Foundation: This chapter defines key terms like AI, XAI, and UX. It explores the different types of AI models, the need for explainability in complex systems, and various XAI methods, including a taxonomy of XAI methods in computer vision.
• Methodology: This chapter presents the research questions and hypothesis driving the thesis. It outlines the methodology used, including the Systematic Literature Research (SLR) conducted to identify UX principles relevant to XAI and the User-centered XAI Design cycle followed to develop and evaluate the prototype system.
• Prototyping and Evaluating an XAI Interface: This chapter focuses on deriving relevant UX principles from the SLR and categorizing them into the UX pyramid. Subsequently, a prototype XAI system called Brain Tumor Assistant (BTA) is developed according to the UCD process. The BTA leverages a pre-trained EfficientNetB0 CNN for brain tumor classification and uses LIME for generating visual explanations. Finally, a user study is conducted to quantify the UX of the BTA with and without the visual explanations, allowing the author to analyze the effect of explanations on various UX dimensions.

Schlüsselwörter (Keywords)

This thesis focuses on the interplay between Explainable Artificial Intelligence (XAI), User Experience (UX), and computer vision. Key areas of interest include the design and evaluation of UX-optimized XAI interfaces, particularly in the context of brain tumor detection. This research incorporates concepts like user-centered design, Convolutional Neural Networks (CNNs), Local Interpretable Model-agnostic Explanations (LIME), and the User Experience Questionnaire (UEQ). The study investigates the impact of visual explanations on dimensions of usability, trust, and user satisfaction.

Frequently Asked Questions

What is Explainable AI (XAI)?

XAI refers to artificial intelligence systems designed to provide human-understandable explanations for their decisions and predictions, moving away from "black box" models.

How does XAI impact User Experience (UX)?

XAI can significantly improve UX by increasing transparency, which in turn enhances user trust, perceived usefulness, and the feeling of control over the system.

What is the "Brain Tumor Assistant" (BTA) prototype?

The BTA is a custom XAI system developed for this thesis that uses a Convolutional Neural Network to classify brain x-rays and provides visual explanations using the LIME method.

What is the LIME method in AI?

LIME stands for Local Interpretable Model-agnostic Explanations. It is a technique used to explain the predictions of any machine learning classifier by approximating it locally with an interpretable model.

How can the quality of XAI be measured?

Quality can be quantified using tools like the User Experience Questionnaire (UEQ), specifically adapted to assess dimensions like trustworthiness, attractiveness, and controllability in XAI systems.

Do visual explanations really increase trust?

Yes, the study conducted in this thesis found that visual explanations have a statistically significant positive effect on user trust and overall system acceptance.