activity
20192026
most citedFACE: Feasible and Actionable Counterfactual Explanations

290 citations · 881 across the 29 of their papers we have counts for

collaborators
Showing 2023Show all

8 papers · 1 filter

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…

cs.HC2023★ 9 cited

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…

cs.LG2023★ 4 cited

(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…

cs.LG2023★ 2 cited

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…

cs.HC2023★ 5 cited

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…

cs.LG2023★ 9 cited

Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness

Edward A. Small, Kacper Sokol, Daniel Manning +2

Group fairness is achieved by equalising prediction distributions between protected sub-populations; individual fairness requires treating similar individuals alike. These two obje…