290 citations · 1.2k across the 29 of their papers we have counts for
7 papers · 1 filter
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…
Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration
Meelis Kull, Miquel Perello-Nieto, Markus Kängsepp +3
Class probabilities predicted by most multiclass classifiers are uncalibrated, often tending towards over-confidence. With neural networks, calibration can be improved by temperatu…
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…
FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency
Kacper Sokol, Raul Santos-Rodriguez, Peter Flach
Today, artificial intelligence systems driven by machine learning algorithms can be in a position to take important, and sometimes legally binding, decisions about our everyday liv…
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…