@inproceedings{56480,
  abstract     = {{As the field of healthcare increasingly adopts artificial intelligence, it becomes important to understand which types of explanations increase transparency and empower users to develop confidence and trust in the predictions made by machine learning (ML) systems. 
In shared decision-making scenarios where doctors cooperate with ML systems to reach an appropriate decision, establishing mutual trust is crucial. In this paper, we explore different approaches to generating explanations in eXplainable AI (XAI) and make their underlying arguments explicit so that they can be evaluated by medical experts.
In particular, we present the findings of a user study conducted with physicians to investigate their perceptions of various types of AI-generated explanations in the context of diagnostic decision support. The study aims to identify the most effective and useful explanations that enhance the diagnostic process. 
In the study, medical doctors filled out a survey to assess different types of explanations. Further, an interview was carried out post-survey to gain qualitative insights on the requirements of explanations incorporated in diagnostic decision support. Overall, the insights gained from this study contribute to understanding the types of explanations that are most effective.}},
  author       = {{Liedeker, Felix and Sanchez-Graillet, Olivia and Seidler, Moana and Brandt, Christian and Wellmer, Jörg and Cimiano, Philipp}},
  location     = {{Santiago de Compostela, Spain}},
  title        = {{{A User Study Evaluating Argumentative Explanations in Diagnostic Decision Support}}},
  year         = {{2024}},
}

@inproceedings{56479,
  abstract     = {{While the importance of explainable artificial intelligence in high-stakes decision-making is widely recognized in existing literature, empirical studies assessing users' perceived value of explanations are scarce. In this paper, we aim to address this shortcoming by conducting an empirical study focused on measuring the perceived value of the following types of explanations: plain explanations based on feature attribution, counterfactual explanations and complex counterfactual explanations. We measure an explanation's value using five dimensions: perceived accuracy, understandability, plausibility, sufficiency of detail, and user satisfaction. Our findings indicate a sweet spot of explanation complexity, with both dimensional and structural complexity positively impacting the perceived value up to a certain threshold.}},
  author       = {{Liedeker, Felix and Düsing, Christoph and Nieveler, Marcel and Cimiano, Philipp}},
  keywords     = {{XAI, Explanation Complexity, User Perception}},
  location     = {{Valetta, Malta}},
  title        = {{{An Empirical Investigation of Users' Assessment of XAI Explanations: Identifying the Sweet-Spot of Explanation Complexity}}},
  year         = {{2024}},
}

@inproceedings{56844,
  author       = {{Battefeld, Dominik and Liedeker, Felix and Cimiano, Philipp and Kopp, Stefan}},
  booktitle    = {{Proceedings of the 1st Workshop on Multimodal, Affective and Interactive eXplainable AI (MAI-XAI)}},
  location     = {{Santiago de Compostela, Spain}},
  title        = {{{ASCODI: An XAI-based interactive reasoning support system for justifiable medical diagnosing}}},
  year         = {{2024}},
}

@inproceedings{56477,
  abstract     = {{We describe a prototype of a Clinical Decision Support System (CDSS) that provides (counterfactual) explanations to support accurate medical diagnosis. The prototype is based on an inherently interpretable Bayesian network (BN). Our research aims to investigate which explanations are most useful for medical experts and whether co-constructing explanations can foster trust and acceptance of CDSS.}},
  author       = {{Liedeker, Felix and Cimiano, Philipp}},
  keywords     = {{Explainable AI, Clinical decision support, Bayesian network, Counterfactual explanations}},
  location     = {{Lissabon}},
  title        = {{{A Prototype of an Interactive Clinical Decision Support System with Counterfactual Explanations}}},
  year         = {{2023}},
}

@inproceedings{56478,
  author       = {{Liedeker, Felix and Cimiano, Philipp}},
  location     = {{Breckenridge, CO, USA }},
  title        = {{{Dynamic Feature Selection in AI-based Diagnostic Decision Support for Epilepsy}}},
  year         = {{2023}},
}