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

Contents

1 Introduction

2 Theoretical Foundation

2.1 Artificial Intelligence (AI)

2.2 Explainable Artificial Intelligence (XAI)

2.2.1 Explainability and Related Terms

2.2.2 Definition

2.2.3 Taxonomy of XAI Methods

2.3 User Experience (UX)

3 Methodology

3.1 Systematic Literature Research (SLR)

3.2 User-centered XAI Design

4 Prototyping and Evaluating an XAI Interface

4.1 Deriving Principles of User Experience

4.1.1 UX Principles in general

4.1.2 UX Principles according to XAI

4.2 Prototyping an XAI Interface for Computer Vision Tasks

4.2.1 Phase 1: Context

4.2.2 Phase 2: User

4.2.3 Phase 3: Solution

4.3 Evaluating an XAI Interface

4.3.1 Construction of an XAI-related Questionnaire

4.3.1.1 User Experience Questionnaire (UEQ)

4.3.1.2 Adapting the UEQ according to XAI

4.3.2 User Study

4.3.2.1 Design and Execution

4.3.2.2 General Results

4.3.2.3 Results from Quantitative Data Analysis

5 Conclusion

Research Objectives & Key Thematics

The primary objective of this thesis is to investigate how visual explanations within AI-based decision support systems (specifically for computer vision tasks like brain tumor detection) influence user experience dimensions, such as usability, trust, and acceptance. The thesis aims to establish a standardized method for quantifying these effects using a user-centered design approach and a validated questionnaire.

• Development of a toolkit comprising 17 UX principles categorized by their relevance to XAI systems.
• Prototyping of a custom XAI system (Brain Tumor Assistant - BTA) based on UCD phases.
• Application of Local Interpretable Model-agnostic Explanations (LIME) for generating visual explanations in medical image analysis.
• Quantitative evaluation of user experience through an adapted User Experience Questionnaire (UEQ) in a controlled A/B test study.
• Statistical assessment of the impact of visual explanations on user trust, attractiveness, controllability, and usefulness.

Excerpt from the Book

Summary of Chapters

1 Introduction: Introduces the research motivation regarding XAI and its impact on end-user trust and usability in decision support systems.

2 Theoretical Foundation: Provides a comprehensive overview of AI, XAI, and UX definitions, including the design of explanation interfaces.

3 Methodology: Details the research methodology, specifically the Systematic Literature Research (SLR) and the user-centered design (UCD) approach.

4 Prototyping and Evaluating an XAI Interface: Describes the extraction of UX principles, the development of the BTA prototype, and the execution and analysis of the user study.

5 Conclusion: Summarizes the key research findings, discusses the impact of visual explanations on user experience, and highlights limitations for future research.

Keywords

Explainable Artificial Intelligence, XAI, User Experience, UX, Usability, Convolutional Neural Network, Brain Tumor Detection, Human-Computer Interaction, Interface Design, Quantitative User Study, User Experience Questionnaire, UEQ, LIME, Decision Support Systems, Visual Explanations.

Frequently Asked Questions

What is the core focus of this research?

The research focuses on closing the gap between purely technical XAI development and user-oriented design, specifically investigating how explanation interfaces affect the user experience of AI-based decision support systems in high-stakes fields like medicine.

Which domains are the central focus of the work?

The work focuses on the intersection of Explainable AI (XAI) and User Experience (UX) design, with a specific practical application in the field of radiology and brain tumor detection.

What is the primary objective of this thesis?

The primary goal is to determine if and how visual explanations in computer vision tasks positively influence specific user experience dimensions, such as trustworthiness, usefulness, controllability, and attractiveness.

Which scientific methodology is applied?

The thesis utilizes a Systematic Literature Research (SLR) to extract UX principles, followed by a user-centered design (UCD) approach to create a prototype, and finally, a quantitative A/B test study to empirically measure UX effects.

What is covered in the main body (Chapter 4)?

Chapter 4 details the extraction of 17 UX principles tailored for XAI, the prototyping of the "Brain Tumor Assistant" (BTA) using Figma, and the evaluation of user responses gathered through an adapted User Experience Questionnaire (UEQ).

Which keywords characterize this work?

The work is characterized by terms such as XAI, User Experience, Usability, Convolutional Neural Networks, Deep Learning, LIME, and Brain Tumor Detection.

How were visual explanations implemented in the prototype?

Visual explanations were implemented using LIME (Local Interpretable Model-agnostic Explanations), a perturbation-based method that segments X-ray images to identify and highlight relevant pixels that contributed most to a specific AI classification.

What was the key finding regarding visual explanations?

The study found that visual explanations have a highly significant positive effect on the dimension of trustworthiness, as well as significant positive impacts on usefulness, attractiveness, and controllability compared to an interface without visual explanations.