249 citations · 431 across the 3 of their papers we have counts for
5 papers
One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency
Kacper Sokol, Peter Flach
The need for transparency of predictive systems based on Machine Learning algorithms arises as a consequence of their ever-increasing proliferation in the industry. Whenever black-…
Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches
Kacper Sokol, Peter Flach
Explanations in Machine Learning come in many forms, but a consensus regarding their desired properties is yet to emerge. In this paper we introduce a taxonomy and a set of descrip…
bLIMEy: Surrogate Prediction Explanations Beyond LIME
Kacper Sokol, Alexander Hepburn, Raul Santos-Rodriguez +1
Surrogate explainers of black-box machine learning predictions are of paramount importance in the field of eXplainable Artificial Intelligence since they can be applied to any type…
FACE: Feasible and Actionable Counterfactual Explanations
Rafael Poyiadzi, Kacper Sokol, Raul Santos-Rodriguez +2
Work in Counterfactual Explanations tends to focus on the principle of "the closest possible world" that identifies small changes leading to the desired outcome. In this paper we a…
HyperStream: a Workflow Engine for Streaming Data
Tom Diethe, Meelis Kull, Niall Twomey +5
This paper describes HyperStream, a large-scale, flexible and robust software package, written in the Python language, for processing streaming data with workflow creation capabili…