6 papers
Verifiable User Simulation for Search and Recommendation Systems
Chenglong Ma, Xinye Wanyan, Danula Hettiachchi +3
Large-language-model (LLM) based user simulation is increasingly adopted for evaluating search engines, recommender systems, and retrieval-augmented generation pipelines, yet most…
Balancing Fairness, Privacy, and Accuracy: A Multitask Adversarial Framework for Centralized Data-Driven Systems
Imesh Ekanayake, Elham Naghizade, Jeffrey Chan
The integration of fairness and privacy in centralized data-driven applications is critical, especially as these systems increasingly influence sectors with significant societal im…
Applying Value Sensitive Design to Location-Based Services: Designing for Shared Spaces and Local Conditions
Hiruni Kegalle, Flora D. Salim, Mark Sanderson +2
Location-Based Services (LBS) such as ride-sharing, accommodation, food delivery, and location-driven social media platforms entangle digital systems with physical spaces, thereby…
Diversity-Augmented Negative Sampling for Implicit Collaborative Filtering
Yueqing Xuan, Kacper Sokol, Mark Sanderson +1
Recommenders built upon implicit collaborative filtering are typically trained to distinguish between users' positive and negative preferences. When direct observations of the latt…
Perfect Counterfactuals in Imperfect Worlds: Modelling Noisy Implementation of Actions in Sequential Algorithmic Recourse
Yueqing Xuan, Kacper Sokol, Mark Sanderson +1
Algorithmic recourse suggests actions to individuals who have been adversely affected by automated decision-making, helping them to achieve the desired outcome. Knowing the recours…
Leveraging Complementary AI Explanations to Mitigate Misunderstanding in XAI
Yueqing Xuan, Kacper Sokol, Mark Sanderson +1
Artificial intelligence explanations can make complex predictive models more comprehensible. To be effective, however, they should anticipate and mitigate possible misinterpretatio…