most citedComprehension Is a Double-Edged Sword: Over-Interpreting Unspecified Information in Intelligible Machine Learning Explanations

9 citations · 19 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2023

Monitoring Sustainable Global Development Along Shared Socioeconomic Pathways

Michelle W. L. Wan, Jeffrey N. Clark, Edward A. Small +2

Sustainable global development is one of the most prevalent challenges facing the world today, hinging on the equilibrium between socioeconomic growth and environmental sustainabil…

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.LG2023★ 1 cited

TraCE: Trajectory Counterfactual Explanation Scores

Jeffrey N. Clark, Edward A. Small, Nawid Keshtmand +5

Counterfactual explanations, and their associated algorithmic recourse, are typically leveraged to understand, explain, and potentially alter a prediction coming from a black-box c…

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★ 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…