249 citations · 598 across the 5 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…
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…
-IRT: A New Item Response Model and its Applications
Yu Chen, Telmo Silva Filho, Ricardo B. C. Prudêncio +2
Item Response Theory (IRT) aims to assess latent abilities of respondents based on the correctness of their answers in aptitude test items with different difficulty levels. In this…