Unraveling The Black Box - Building Understandable AI Through Strategic Explanation and User-based Design

Abstract

The pervasive integration of Artificial Intelligence (AI) in society presents both opportunities and challenges, with the black-box issue emerging as a significant obstacle in realizing the full potential of AI. The opaque nature of AI decision-making processes impedes user understanding, particularly among non-technical individuals, raising concerns about the reliability of AI recommendations. Therefore, how to help users understand AI decisionmaking has become an urgent task. This thesis aims to assist developers in contemplating how to construct AI that users can understand. To build understandable AI, researchers have proposed many theories, methods, and frameworks in existing research. However, there are still limitations and challenges in current research. To address these challenges and finish the research aim, starting with a discussion on transparency and interpretability, the thesis elaborates on how to strategically explain to users within three dimensions: simplifying algorithm, appropriate information disclosure, and high-level collaboration. Furthermore, the thesis conducts surveys on users in four high-stakes areas, establishing AI explainability principles based on three stages, conceptualization, construction, and measurement. In addition to these primary contributions, the thesis also covers some supportive work, including challenges faced by explainable AI, user-centered development, and automation trust. These works lay a solid foundation for addressing research questions and achieving research objectives, while also providing room for contemplation in future research.