249 citations · 487 across the 10 of their papers we have counts for
14 papers · 1 filter
Uncertainty Quantification as a Principled Foundation for Explainable Artificial Intelligence: A Case Study of Counterfactual Explanations
Kacper Sokol, Santo M. A. R. Thies, Eyke Hüllermeier
In this paper we argue that, to its detriment, transparency research overlooks many foundational concepts of artificial intelligence. As an illustrating example we focus on uncerta…
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…
Counterfactual Explanations for Clustering Models
Aurora Spagnol, Kacper Sokol, Pietro Barbiero +2
Clustering algorithms rely on complex optimisation processes that may be difficult to comprehend, especially for individuals who lack technical expertise. While many explainable ar…
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…
(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Comprehensibility
Kacper Sokol, Julia E. Vogt
Ante-hoc interpretability has become the holy grail of explainable artificial intelligence for high-stakes domains such as healthcare; however, this notion is elusive, lacks a wide…
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…