10 papers
Consensus vs. Dissent: Dynamic LLM Modeling of Subjective Preferences in Group Recommenders
Cedric Waterschoot, Nava Tintarev, Francesco Barile
Previous work in group recommender systems has demonstrated a sensitivity to the distribution of preferences within a group. Specifically, the selection of the preference aggregati…
Keeping an Eye on AI: A Framework for Effective Human Oversight of AI Systems
Susanne Gaube, Markus Langer, Tim Miller +17
The use of Artificial Intelligence (AI) in high-risk, decision-making scenarios presents technical, safety, and normative challenges; problems that may only be ameliorated by human…
A Little More Like This: Text-to-Image Retrieval with Vision-Language Models Using Relevance Feedback
Bulat Khaertdinov, Mirela Popa, Nava Tintarev
Large vision-language models (VLMs) enable intuitive visual search using natural language queries. However, improving their performance often requires fine-tuning and scaling to la…
Consistent Explainers or Unreliable Narrators? Understanding LLM-generated Group Recommendations
Cedric Waterschoot, Nava Tintarev, Francesco Barile
Large Language Models (LLMs) are increasingly being implemented as joint decision-makers and explanation generators for Group Recommender Systems (GRS). In this paper, we evaluate…
Patient-Centred Explainability in IVF Outcome Prediction
Adarsa Sivaprasad, Ehud Reiter, David McLernon +3
This paper evaluates the user interface of an in vitro fertility (IVF) outcome prediction tool, focussing on its understandability for patients or potential patients. We analyse fo…
The Pitfalls of Growing Group Complexity: LLMs and Social Choice-Based Aggregation for Group Recommendations
Cedric Waterschoot, Nava Tintarev, Francesco Barile
Large Language Models (LLMs) are increasingly applied in recommender systems aimed at both individuals and groups. Previously, Group Recommender Systems (GRS) often used social cho…