Nowadays, algorithms analyze user data and affect the decision-making process for millions of people on matters like employment, insurance and loan rates, and even criminal justice. However, these algorithms that serve critical roles in many industries have their own biases that can result in discrimination and unfair decision-making.
Explainable Artificial Intelligence (XAI) systems can be a solution to predictable and accountable AI by explaining AI decision-making processes for end users and therefore increase user awareness and prevent bias and discrimination. The broad spectrum of research on XAI, including designing interpretable models, explainable user interfaces, and human-subject studies of XAI systems are sought in different disciplines such as machine learning, human-computer interactions (HCI), and visual analytics. The mismatch in objectives for the scholars to define, design, and evaluate the concept of XAI may slow down the overall advances of end-to-end XAI systems.
My research aims to converge knowledge behind design and evaluation of XAI systems between multiple disciplines to further support key benefits of algorithmic transparency and interpretability. To this end, I propose a comprehensive design and evaluation framework for XAI systems with step-by-step guidelines to pair different design goals with their evaluation methods for iterative system design cycles in multidisciplinary teams. This dissertation presents a comprehensive XAI design and evaluation framework to provide guidance for different design goals and evaluation approaches in XAI systems.
After a thorough review of XAI research in the fields of machine learning, visualization, and HCI, I present a categorization of XAI design goals and evaluation methods and show a mapping between design goals for different XAI user groups and their evaluation methods.
From my findings, I present a design and evaluation framework for XAI systems (Objective 1) to address the relation between different system design needs. The framework provides recommendations for different goals and ready-to-use tables of evaluation methods for XAI systems. The importance of this framework is in providing guidance for researchers on different aspects of XAI system design in multidisciplinary team efforts.
Then, I demonstrate and validate the proposed framework (Objective 2) through one end-to-end XAI system case study and two examples by analysis of previous XAI systems in terms of our framework. I present two contributions to my XAI design and evaluation framework to improve evaluation methods for XAI system
