Explainability of AI systems is branded as a solution to the black box problem. The EU AI Act introduces AI explainability as a requirement for high-risk AI systems, including a right to explanation. However, understanding the wording of the requirements and the related provider and deployer obligations …

As artificial intelligence (AI) systems are increasingly deployed in high-risk financial decision-making contexts, the demand for transparency and interpretability becomes critical. Explainable AI (XAI) has emerged as a key research domain addressing these needs. While most existing XAI studies emphasize …

Industrial Cyber-Physical Systems (CPS) are sensitive infrastructure from both safety and economics perspectives, making their reliability critically important. Machine Learning (ML), specifically deep learning, is increasingly integrated in industrial CPS, but the inherent complexity of ML models results …

Over de betrouwbaarheid van AI, en de mogelijkheden binnen verschillende sectoren.

The workload in the healthcare sector is increasing, requiring the need for innovative solutions. One such solution is for AI to assist in clinical decision-making by extracting information from patient’s records. To ensure healthcare professionals stay in the lead, the reasoning of the AI should be …

It is now widely accepted that decisions made by AI systems must be explainable to their users. However, in practice, it often remains unclear how this explainability should be concretely implemented. This is especially important for nontechnical users, such as claims assessors at insurance companies, …

This guide was developed for designers and developers of AI systems, with the goal of ensuring that these systems are sufficiently explainable. Sufficient here means that it meets the legal re quirements from AI Act and GDPR and that users can use the system properly. Explainability of decisions is …

Trust in AI is crucial for effective and responsible use in high-stakes sectors like healthcare and finance. One of the most commonly used techniques to mitigate mistrust in AI and even increase trust is the use of Explainable AI models, which enables human understanding of certain decisions made by …

Explainable Artificial Intelligence (XAI) aims to provide insights into the inner workings and the outputs of AI systems. Recently, there’s been growing recognition that explainability is inherently human-centric, tied to how people perceive explanations. Despite this, there is no consensus in the research …

One aspect of a responsible application of Artificial Intelligence (AI) is ensuring that the operation and outputs of an AI system are understandable for non-technical users, who need to consider its recommendations in their decision making. The importance of explainable AI (XAI) is widely acknowledged; …