activity
20232026
most citedLeveraging Complementary AI Explanations to Mitigate Misunderstanding in XAI

2 citations · 3 across the 8 of their papers we have counts for

collaborators

8 papers

cs.IR2026

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…

cs.LG2026

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…

cs.HC2026

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…

cs.IR2025

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…

cs.HC2025★ 2 cited

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…

cs.LG2024★ 1 cited

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…

cs.AI2024

Long-term Fairness in Ride-Hailing Platform

Yufan Kang, Jeffrey Chan, Wei Shao +2

Matching in two-sided markets such as ride-hailing has recently received significant attention. However, existing studies on ride-hailing mainly focus on optimising efficiency, and…

cs.LG2023

Counterfactual Explanations via Locally-guided Sequential Algorithmic Recourse

Edward A. Small, Jeffrey N. Clark, Christopher J. McWilliams +4

Counterfactuals operationalised through algorithmic recourse have become a powerful tool to make artificial intelligence systems explainable. Conceptually, given an individual clas…