Explainable Artificial Intelligence: Designing human-centric assessment system interfaces to increase explainability and trustworthiness of artificial intelligence in medical applications

Art der Abschlussarbeit

Status der Arbeit


Hintergrundinformationen zu der Arbeit

With the all pervasive use of artificial intelligence in current technological advances the
medical domain is no exception. Performant but complex AI models, such as deep neural
networks, can be used for decision support systems for medical professionals. However,
such AI models are commonly referred to as a "black box" which humans struggle to
understand. This lack of understanding leads to trust and compliance issues, especially
in the medical context, where the consequences can be severe. Combining the HCI with
the XAI domain allows for designing and developing a human-centric AI assessment
system to facilitate the AI model’s understandability and trustworthiness for the user.
As part of this thesis a prototype was conceptualized based on user-centered research
and XAI literature, implemented as a flexible browser-based application and evaluated
with medical students. The results show connections between interactive explanations,
understandability and trustworthiness of AI models. A summative evaluation of the
prototype showed that the user’s subjective understanding of the AI model increased
through the interaction with the system. Furthermore the user’s perceived trustworthiness
of the AI model decreased. From this finding we can conclude that the presented
interactive explanations are suitable for moderating the user’s subjective understanding
and perceived trustworthiness of the AI model. Additionally, guidance in HCI was
observed to reduce the explanation satisfaction for the users surveyed, while having no
significant effects on perceived understandability and trustworthiness of the AI model.

Philipp Dominik Bzdok


Juli 2021


Jan. 2022