Workshop on Human-In-the-Loop Data Analytics (HILDA 2017)

Interpreting Black-Box Classifiers Using
Instance-Level Visual Explanations

Paolo Tamagnini, Josua Krause, Aritra Dasgupta, Enrico Bertini

Rivelo user interface in action.

Abstract

To realize the full potential of machine learning in diverse real-world domains, it is necessary for model predictions to be readily interpretable and actionable for the human in the loop. Analysts, who are the users but not the developers of machine learning models, often do not trust a model because of the lack of transparency in associating predictions with the underlying data space. To address this problem, we propose Rivelo, a visual analytics interface that enables analysts to understand the causes behind predictions of binary classifiers by interactively exploring a set of instance-level explanations. These explanations are model-agnostic, treating a model as a black box, and they help analysts in interactively probing the high-dimensional binary data space for detecting features relevant to predictions. We demonstrate the utility of the interface with a case study analyzing a random forest model on the sentiment of Yelp reviews about doctors.

[demo] [pdf] [github]

Bibtex

@article{rivelo,
  author={Tamagnini, Paolo and Krause, Josua and Dasgupta, Aritra and Bertini, Enrico},
  journal={Workshop on Human-In-the-Loop Data Analytics},
  title={Interpreting Black-Box Classifiers Using Instance-Level Visual Explanations},
  year={2017},
  month={May},
  doi={10.1145/3077257.3077260},
  isbn={978-1-4503-5029-7}
}