Katrien Verbert

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Explainability methods

Human-centered AI: how can we support end-users to interact with AI?

Human-centered AI: how can we support end-users to interact with AI?

Human-centered AI: how can we support lay users to understand AI?

Explaining job recommendations: a human-centred perspective

Explaining recommendations: design implications and lessons learned

Designing Learning Analytics Dashboards: Lessons Learned

Human-centered AI: towards the next generation of interactive and adaptive explanation methods

Explainable AI for non-expert users

Towards the next generation of interactive and adaptive explanation methods

Personalized food recommendations: combining recommendation, visualization and augmented reality techniques for healthier food decision-making

Explaining and Exploring Job Recommendations: a User-driven Approach for Interacting with Knowledge-based Job Recommender Systems

Learning analytics for feedback at scale

Interactive recommender systems and dashboards for learning

Interactive recommender systems: opening up the “black box”