2 citations · 2 across the 4 of their papers we have counts for
5 papers
Statistical Foundations of LLM-based A/B Testing: A Surrogacy Framework for Human Causal Inference
Joel Persson, Mårten Schultzberg, Sebastian Ankargren
Organizations and researchers show increasing interest in using large language models (LLMs) in place of human participants in A/B tests, in the hope of experimenting faster and at…
Logging Policy Design for Off-Policy Evaluation
Connor Douglas, Joel Persson, Foster Provost
Off-policy evaluation (OPE) estimates the value of a target treatment policy (e.g., a recommender system) using data collected by a different logging policy. It enables high-stakes…
Detecting and Mitigating Group Bias in Heterogeneous Treatment Effects
Joel Persson, Jurriën Bakker, Dennis Bohle +2
Heterogeneous treatment effects (HTEs) are increasingly estimated using machine learning models that produce highly personalized predictions of treatment effects. In practice, howe…
Auditing a Dutch Public Sector Risk Profiling Algorithm Using an Unsupervised Bias Detection Tool
Floris Holstege, Mackenzie Jorgensen, Kirtan Padh +5
Algorithms are increasingly used to automate or aid human decisions, yet recent research shows that these algorithms may exhibit bias across legally protected demographic groups. H…
EventChat: Implementation and user-centric evaluation of a large language model-driven conversational recommender system for exploring leisure events in an SME context
Hannes Kunstmann, Joseph Ollier, Joel Persson +1
Large language models (LLMs) present an enormous evolution in the strategic potential of conversational recommender systems (CRS). Yet to date, research has predominantly focused u…