Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
cетевое издание
issn 2310-6018

Explainable artificial intelligence and methods for interpreting results

idShevskaya N.V.

UDC 004.891.2
DOI: 10.26102/2310-6018/2021.33.2.024

  • Abstract
  • List of references
  • About authors

Artificial intelligence systems are used in many areas of human life support for example finance or medicine. Every year intelligent systems process more and more data and make more and more decisions. All these decisions have an increasing impact on the fate of people. The corner-stone is a distrust of completely non-human, autonomous artificial intelligence systems. The key to distrust lies in the misunderstanding of why intelligent systems make this or that decision, based on what beliefs such systems operate (and whether they have their own beliefs or only those that were given to them by the developers). To solve the problem of “distrust” in such sys-tems, the methods of explainable artificial intelligence have been used. This article provides a brief overview of the most popular methods in the academic environment such methods as PDP, SHAP, LIME, DeepLIFT, permutation importance, ICE plots. Practical exercises demonstrate the ease of application of PDP and SHAP methods, as well as the convenience of "reading" the graphical results of these methods, which explain the constructed decision tree model and ran-dom forest model on the example of a small set of sales data

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Shevskaya Natalya Vladimirovna

Scopus | ORCID | eLibrary |

Saint Petersburg Electrotechnical University "LETI"

Saint Petersburg, Russia

Keywords: artificial intelligence, explainable artificial intelligence, interpretable artificial intelligence, explainability, interpretability, XAI, PDP, SHAP

For citation: Shevskaya N.V. Explainable artificial intelligence and methods for interpreting results. Modeling, Optimization and Information Technology. 2021;9(2). Available from: https://moitvivt.ru/ru/journal/pdf?id=1005 DOI: 10.26102/2310-6018/2021.33.2.024 (In Russ).

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Accepted 30.07.2021

Published 04.08.2021