51 citations · 111 across the 13 of their papers we have counts for
16 papers
Faithfulness Without Anchors: A Position Paper on the Structural Misalignments of Explainable AI Faithfulness Metrics for Feature Importance
Marco Zullich, Emily Schiller, Ivan Gentile +4
Explainable Artificial Intelligence (XAI) tools have often been presented as methods to increase trust in machine learning models by making aspects of their algorithms more human-u…
The Directed Prediction Change - Efficient and Trustworthy Fidelity Assessment for Local Feature Attribution Methods
Kevin Iselborn, David Dembinsky, Adriano Lucieri +1
The utility of an explanation method critically depends on its fidelity to the underlying machine learning model. Especially in high-stakes medical settings, clinicians and regulat…
LD-ViCE: Latent Diffusion Model for Video Counterfactual Explanations
Payal Varshney, Adriano Lucieri, Christoph Balada +2
Video-based AI systems are increasingly adopted in safety-critical domains such as autonomous driving and healthcare. However, interpreting their decisions remains challenging due…
Towards Facilitated Fairness Assessment of AI-based Skin Lesion Classifiers Through GenAI-based Image Synthesis
Ko Watanabe, Stanislav Frolov, Aya Hassan +3
Recent advances in deep learning and on-device inference could transform routine screening for skin cancers. Along with the anticipated benefits of this technology, potential dange…
Unifying VXAI: A Systematic Review and Framework for the Evaluation of Explainable AI
David Dembinsky, Adriano Lucieri, Stanislav Frolov +3
Modern AI systems frequently rely on opaque black-box models, most notably Deep Neural Networks, whose performance stems from complex architectures with millions of learned paramet…
Discovering Concept Directions from Diffusion-based Counterfactuals via Latent Clustering
Payal Varshney, Adriano Lucieri, Christoph Balada +2
Concept-based explanations have emerged as an effective approach within Explainable Artificial Intelligence, enabling interpretable insights by aligning model decisions with human-…
Generating Counterfactual Trajectories with Latent Diffusion Models for Concept Discovery
Payal Varshney, Adriano Lucieri, Christoph Balada +2
Trustworthiness is a major prerequisite for the safe application of opaque deep learning models in high-stakes domains like medicine. Understanding the decision-making process not…
Privacy Meets Explainability: A Comprehensive Impact Benchmark
Saifullah Saifullah, Dominique Mercier, Adriano Lucieri +2
Since the mid-10s, the era of Deep Learning (DL) has continued to this day, bringing forth new superlatives and innovations each year. Nevertheless, the speed with which these inno…
Revisiting the Shape-Bias of Deep Learning for Dermoscopic Skin Lesion Classification
Adriano Lucieri, Fabian Schmeisser, Christoph Peter Balada +3
It is generally believed that the human visual system is biased towards the recognition of shapes rather than textures. This assumption has led to a growing body of work aiming to…
ExAID: A Multimodal Explanation Framework for Computer-Aided Diagnosis of Skin Lesions
Adriano Lucieri, Muhammad Naseer Bajwa, Stephan Alexander Braun +3
One principal impediment in the successful deployment of AI-based Computer-Aided Diagnosis (CAD) systems in clinical workflows is their lack of transparent decision making. Althoug…
Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series Classification
Dominique Mercier, Adriano Lucieri, Mohsin Munir +2
With the advent of machine learning in applications of critical infrastructure such as healthcare and energy, privacy is a growing concern in the minds of stakeholders. It is pivot…
XAI Handbook: Towards a Unified Framework for Explainable AI
Sebastian Palacio, Adriano Lucieri, Mohsin Munir +3
The field of explainable AI (XAI) has quickly become a thriving and prolific community. However, a silent, recurrent and acknowledged issue in this area is the lack of consensus re…
Deep Learning Based Decision Support for Medicine -- A Case Study on Skin Cancer Diagnosis
Adriano Lucieri, Andreas Dengel, Sheraz Ahmed
Early detection of skin cancers like melanoma is crucial to ensure high chances of survival for patients. Clinical application of Deep Learning (DL)-based Decision Support Systems…
Achievements and Challenges in Explaining Deep Learning based Computer-Aided Diagnosis Systems
Adriano Lucieri, Muhammad Naseer Bajwa, Andreas Dengel +1
Remarkable success of modern image-based AI methods and the resulting interest in their applications in critical decision-making processes has led to a surge in efforts to make suc…
On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors
Adriano Lucieri, Muhammad Naseer Bajwa, Stephan Alexander Braun +3
Deep learning based medical image classifiers have shown remarkable prowess in various application areas like ophthalmology, dermatology, pathology, and radiology. However, the acc…
Explaining AI-based Decision Support Systems using Concept Localization Maps
Adriano Lucieri, Muhammad Naseer Bajwa, Andreas Dengel +1
Human-centric explainability of AI-based Decision Support Systems (DSS) using visual input modalities is directly related to reliability and practicality of such algorithms. An oth…