An Information-Theoretic Approach to Personalized Explainable Machine Learning

Alex Jung, Pedro H. J. Nardelli

Research output: Contribution to journalArticleScientificpeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)825-829
JournalIEEE Signal Processing Letters
Volume27
DOIs
Publication statusPublished - 7 May 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • Machine learning, vector spaces, Healthcare classifications, CaseMix
  • Explainable artificial intelligence
  • artificial intelligence
  • INFORMATION VISUALIZATION
  • VISUALIZATION
  • human-computer interaction
  • data science

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