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 …

Artificial intelligence (AI) is transforming language access services in healthcare, making interpretation and translation faster and more scalable than ever before. Given that there are approximately 281 million international migrants as of 2024 [1], leveraging AI technology for mitigating language …

This thesis explores how organizations can build stronger trust and transparency in AI-driven marketing. It examines customer concerns around data use, personalization, and algorithmic decision-making, and identifies practices that make AI more understandable, fair, and responsible. Through interviews …

Artificial intelligence (AI) is an emerging technology and offers opportunities for developing innovative products and services. Traditionally, innovation process models are tools to support innovation. However, existing innovation process models are criticized for being generic, rigid, and resource-intensive, …

Background: As more and more older adults prefer to stay in their homes as they age, there’s a need for technology to support this. A relevant technology is Artificial Intelligence (AI)-driven lifestyle monitoring, utilizing data from sensors placed in the home. This technology is not intended to replace …

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 …

As artificial intelligence (AI) reshapes hiring, organizations increasingly rely on AI-enhanced selection methods such as chatbot-led interviews and algorithmic resume screening. While AI offers efficiency and scalability, concerns persist regarding fairness, transparency, and trust. This qualitative …

Het is inmiddels breed geaccepteerd dat beslissingen die door AI-systemen worden genomen, uitlegbaar moeten zijn aan hun gebruikers. Toch blijft het in de praktijk vaak onduidelijk hoe die uitlegbaarheid concreet vorm moet krijgen. Vooral voor niet-technische gebruikers, zoals scha-debeoordelaars bij …

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, …

In deze keynote verken ik de balans tussen menselijke expertise en AI: leidt AI tot cognitieve luiheid? En wat zegt de wetenschap hierover? Hoe zet je AI in zonder je vakmanschap te verliezen? Wat gebeurt er als je te veel uit handen geeft aan tools? En wanneer gebruik je technologie juist om beter te …