9 citations · 20 across the 8 of their papers we have counts for
8 papers
Explanation Navigator: Rectifying Out-of-Scope Human Interpretations of Leaky AI Explanations through Conversational Guidance
Yueqing Xuan, Kacper Sokol, Danula Hettiachchi
As explanations of artificial intelligence systems proliferate, their recipients must grasp not only what they convey but also recognise what they cannot. We conducted an interview…
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…
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…
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…
Comprehension Is a Double-Edged Sword: Over-Interpreting Unspecified Information in Intelligible Machine Learning Explanations
Yueqing Xuan, Edward Small, Kacper Sokol +2
Automated decision-making systems are becoming increasingly ubiquitous, which creates an immediate need for their interpretability and explainability. However, it remains unclear w…
Navigating Explanatory Multiverse Through Counterfactual Path Geometry
Kacper Sokol, Edward Small, Yueqing Xuan
Counterfactual explanations are the de facto standard when tasked with interpreting decisions of (opaque) predictive models. Their generation is often subject to technical and doma…
Helpful, Misleading or Confusing: How Humans Perceive Fundamental Building Blocks of Artificial Intelligence Explanations
Edward Small, Yueqing Xuan, Danula Hettiachchi +1
Explainable artificial intelligence techniques are developed at breakneck speed, but suitable evaluation approaches lag behind. With explainers becoming increasingly complex and a…
Evaluating and Addressing Fairness Across User Groups in Negative Sampling for Recommender Systems
Yueqing Xuan, Kacper Sokol, Mark Sanderson +1
Recommender systems trained on implicit feedback data rely on negative sampling to distinguish positive items from negative items for each user. Since the majority of positive inte…