Editable machine learning models? A rule-based framework for user studies of explainability

Abstract

So far, most user studies dealing with comprehensibility of machine learning models have used questionnaires or surveys to acquire input from participants. In this article, we argue that compared to questionnaires, the use of an adapted version of a real machine learning interface can yield a new level of insight into what attributes make a machine learning model interpretable, and why. Also, we argue that interpretability research also needs to consider the task of humans editing the model, not least due to the existing or forthcoming legal requirements on the right of human intervention. In this article, we focus on rule models as these are directly interpretable as well as editable. We introduce an extension of the EasyMiner system for generating classification and explorative models based on association rules. The presented web-based rule editing software allows the user to perform common editing actions such as modify rule (add or remove attribute), delete rule, create new rule, or reorder rules. To observe the effect of a particular edit on predictive performance, the user can validate the rule list against a selected dataset using a scoring procedure. The system is equipped with functionality that facilitates its integration with crowdsourcing platforms commonly used to recruit participants.

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Notes

  1. 1.

    http://dmg.org/pmml/v4-4/GeneralStructure.html.

  2. 2.

    It should be noted that the current version of the editor does not meet the requirements of the particular study by Muggleton et al. (2018), since some syntactical constructs necessary for the expression of ILP rules are not supported.

  3. 3.

    In this paper, we do not consider the more expressive rules based on the GUHA method (Hájek et al. 1966) that earlier versions of EasyMiner could also process.

  4. 4.

    The rules are sorted by confidence, support and antecedent length, which is the number of attribute-value pairs in the condition of the rule. For confidence and support, the higher value is better. For antecedent length, the shorter (and simpler) antecedent is preferred.

  5. 5.

    Sorted by confidence, support and length as noted above.

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Acknowledgements

This research was supported by long term institutional support of research activities and grant IGA 33/2018 of the University of Economics, Prague. Author contributions: SV implemented the system and edited the article, TK conceived the research, wrote the article and organised the internal user studies.

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Correspondence to Tomáš Kliegr.

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Vojíř, S., Kliegr, T. Editable machine learning models? A rule-based framework for user studies of explainability. Adv Data Anal Classif 14, 785–799 (2020). https://doi.org/10.1007/s11634-020-00419-2

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Keywords

  • Rule learning
  • User experiment
  • Crowdsourcing
  • Explainable Artificial Intelligence
  • Cognitive Computing
  • Legal compliance

Mathematics Subject Classification

  • 68T30