5 papers
Explainable AI for the EU Right to Explanation: A Systematic Review of the Law-XAI Translation Gap
Benjamin Fresz, Elena Dubovitskaya, Marco F. Huber
When algorithms make or influence consequential decisions---about loan eligibility, hiring, or healthcare---EU law grants affected individuals a Right to Explanation. Yet whether (…
The Contribution of XAI for the Safe Development and Certification of AI: An Expert-Based Analysis
Benjamin Fresz, Vincent Philipp Göbels, Safa Omri +5
Developing and certifying safe - or so-called trustworthy - AI has become an increasingly salient issue, especially in light of upcoming regulation such as the EU AI Act. In this c…
Constraint-Data-Value-Maximization: Utilizing Data Attribution for Effective Data Pruning in Low-Data Environments
Danilo Brajovic, David A. Kreplin, Marco F. Huber
Attributing model behavior to training data is an evolving research field. A common benchmark is data removal, which involves eliminating data instances with either low or high val…
Efficiently Transforming Neural Networks into Decision Trees: A Path to Ground Truth Explanations with RENTT
Helena Monke, Benjamin Fresz, Marco Bernreuther +2
Although neural networks are a powerful tool, their widespread use is hindered by the opacity of their decisions and their black-box nature, which result in a lack of trustworthine…
From Confusion to Clarity: ProtoScore -- A Framework for Evaluating Prototype-Based XAI
Helena Monke, Benjamin Sae-Chew, Benjamin Fresz +1
The complexity and opacity of neural networks (NNs) pose significant challenges, particularly in high-stakes fields such as healthcare, finance, and law, where understanding decisi…