A1 Journal article (refereed), original research

An Information-Theoretic Approach to Personalized Explainable Machine Learning

Open Access publication

Publication Details
Authors: Jung Alexander, Nardelli Pedro
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Publication year: 2020
Language: English
Related Journal or Series Information: IEEE Signal Processing Letters
ISSN: 1070-9908
eISSN: 1558-2361
JUFO-Level of this publication: 2
Open Access: Open Access publication
Location of the parallel saved publication: http://urn.fi/URN:NBN:fi-fe2020051435520


Automated decision making is used routinely throughout our every-day life. Recommender systems decide which jobs, movies, or other user profiles might be interesting to us. Spell checkers help us to make good use of language. Fraud detection systems decide if a credit card transactions should be verified more closely. Many of these decision making systems use machine learning methods that fit complex models to massive datasets. The successful deployment of machine learning (ML) methods to many (critical) application domains crucially depends on its explainability. Indeed, humans have a strong desire to get explanations that resolve the uncertainty about experienced phenomena like the predictions and decisions obtained from ML methods. Explainable ML is challenging since explanations must be tailored (personalized) to individual users with varying backgrounds. Some users might have received university-level education in ML, while other users might have no formal training in linear algebra. Linear regression with few features might be perfectly interpretable for the first group but might be considered a black-box by the latter.We propose a simple probabilistic model for the predictions and user knowledge. This model allows to study explainable ML using information theory. Explaining is here considered as the task of reducing the “surprise” incurred by a prediction. We quantify the effect of an explanation by the conditional mutual information between the explanation and prediction, given the user background.

Last updated on 2020-05-06 at 08:02

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