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Prolog-based agnostic explanation module for structured pattern classification

arXiv 2112.12641 ↗First posted 23 Dec 2021cs.LGcited by 0

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Abstract (arXiv)

This paper presents a Prolog-based reasoning module to generate counterfactual explanations given the predictions computed by a black-box classifier. The proposed symbolic reasoning module can also resolve what-if queries using the ground-truth labels instead of the predicted ones. Overall, our approach comprises four well-defined stages that can be applied to any structured pattern classification problem. Firstly, we pre-process the given dataset by imputing missing values and normalizing the numerical features. Secondly, we transform numerical features into symbolic ones using fuzzy clustering such that extracted fuzzy clusters are mapped to an ordered set of predefined symbols. Thirdly, we encode instances as a Prolog rule using the nominal values, the predefined symbols, the decision classes, and the confidence values. Fourthly, we compute the overall confidence of each Prolog rule using fuzzy-rough set theory to handle the uncertainty caused by transforming numerical quantities into symbols. This step comes with an additional theoretical contribution to a new similarity function to compare the previously defined Prolog rules involving confidence values. Finally, we implement a chatbot as a proxy between human beings and the Prolog-based reasoning module to resolve natural language queries and generate counterfactual explanations. During the numerical simulations using synthetic datasets, we study the performance of our system when using different fuzzy operators and similarity functions. Towards the end, we illustrate how our reasoning module works using different use cases.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 39 references that are in the map. First posted on arXiv 23 Dec 2021.

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References · 21

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
  4. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,089
  5. 2018A Survey Of Methods For Explaining Black Box Modelscited by 973
  6. 2016The Mythos of Model Interpretabilitycited by 721
  7. 2019Explaining Machine Learning Classifiers through Diverse Counterfactual Explanationscited by 614
  8. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  9. 2019Neural Probabilistic Logic Programming in DeepProbLogcited by 394
  10. 2017Methods for Interpreting and Understanding Deep Neural Networkscited by 345
  11. 2020Neural Additive Models: Interpretable Machine Learning with Neural Netscited by 319
  12. 2019Interpretable machine learning: definitions, methods, and applicationscited by 305
9 more
  1. 2019FACE: Feasible and Actionable Counterfactual Explanationscited by 233
  2. 2016Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Modelscited by 213
  3. 2020Problems with Shapley-value-based explanations as feature importance measurescited by 200
  4. 2018Local Rule-Based Explanations of Black Box Decision Systemscited by 166
  5. 2019Explainable Machine Learning for Scientific Insights and Discoveriescited by 166
  6. 2020Multi-Objective Counterfactual Explanationscited by 157
  7. 2018Model Agnostic Supervised Local Explanationscited by 83
  8. 2020What Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model Conversationscited by 11
  9. 2021A fuzzy-rough uncertainty measure to discover bias encoded explicitly or implicitly in features of structured pattern classification datasetscited by 5

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