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Interpretable Machine Learning in Social Sciences: Use Cases and Limitations
P. 319–331.
The increasing use of intelligent technologies, the development and implementation of machine learning systems in various spheres of life require explaining machine learning-based decisions in such systems. This need for interpretation leads to the increasing development of new methods for interpreting machine learning models and their more intense use in real systems. The paper reviews existing studies with applications of the interpretable machine learning (IML) methods in social sciences and summarizes results using bibliometric analysis. In total, seven research topics were described based on 210 papers. Moreover, the paper discusses the opportunities, limitations, and challenges of the interpretable machine learning approach in social science research.
Ramos-Soto O., Perez-Zarate E., Ramos-Frutos J. et al., Machine Learning and Knowledge Extraction 2026 Vol. 8 No. 7 Article 173
Added: September 18, 2026
Avdoshin S. M., Pesotskaya E. Y., Информационные технологии 2026 Т. 32 № 4 С. 185–194
With the rapid advancement of artificial intelligence, and deep learning in particular, models have emerged that are capable of delivering highly accurate predictions. However, the internal logic of such models remains difficult to interpret—an issue of critical importance, especially in domains where the correctness of an algorithm directly affects high-stakes decision-making. One promising avenue for ...
Added: May 8, 2026
Avdoshin S. M., Pesotskaya E. Y., Business Informatics 2026 Vol. 20 No. 1 P. 7–28
The rapid development of artificial intelligence (AI) is accompanied by increasing computational
complexity and decreasing model transparency, which significantly limits its adoption in critical
domains that require a high level of trust, interpretability, and justification of decisions. Under these
conditions, the field of Explainable Artificial Intelligence (XAI) has gained particular importance as it
focuses on approaches and technologies that ...
Added: May 8, 2026
Glushko A., Neznanov A., В кн.: Перспективные материалы и технологии (ПМТ-2024) : Сборник докладов Международной научно-технической конференции ИПТИП РТУ МИРЭА, Москва, 12–16 апреля 2024 годаТ. 1.: М.: РТУ МИРЭА, 2024. С. 380–385.
In this paper we would like to discuss the basic principles, design decisions and tools that formed the basis of a software system for analyzing the results of real experiments and performing computational experiments in chemistry and materials science. With this work we aim to formalize knowledge at multiple levels and improving the efficiency of ...
Added: April 29, 2026
Glushko A., Neznanov A., В кн.: Перспективные материалы и технологии (ПМТ-2025) : Сборник докладов Национальной научно-технической конференции с международным участием, Москва, 07–12 апреля 2025 года.: М.: РТУ МИРЭА, 2025. С. 651–657.
Most of scientific research involves the use of computational tools, while researchers have low IT competences. The efficiency of working with computational modules for scientific research can be significantly increased by guaranteeing reproducibility, interactivity, and reusability of producing artifacts. This can be achieved by using a reactive environment with a standardized set of UI controls, ...
Added: April 29, 2026
Sergei O. Kuznetsov, Parakal E. G., Lecture Notes in Networks and Systems 2023 Vol. 776 P. 423–434
Inherently explainable Machine Learning (ML) models are able to provide explanations for their predictions by virtue of their construction. The explanations of a ML model are more comprehensible if they are expressed in terms of its input features. Our paper proposes an inherently explainable pipeline for document classification using pattern structures and Abstract Meaning Representation ...
Added: February 5, 2024
Shalileh S., Koptseva A., Shishkovskaya T. et al., Доклады Российской академии наук. Математика, информатика, процессы управления (ранее - Доклады Академии Наук. Математика) 2023 Т. 514 № 2 С. 242–249
This paper represents our research to (i) propose an artificial intelligence, AI-based solution to identify depression and (ii) investigate our psychiatric knowledge. Concerning the first objective, we collected and annotated a new audio data set, and scrutinized the performance of eight regression approaches. Our studies showed that k-nearest neighbor and random forest form the group ...
Added: February 2, 2024
Baklanova V., Kurkin A., Teplova T., China Finance Review International 2024 Vol. 14 No. 3 P. 522–548
Purpose – The primary objective of this research is to provide a precise interpretation of the constructed
machine learning model and produce definitive summaries that can evaluate the influence of investor sentiment on the overall sales of non-fungible token (NFT) assets. To achieve this objective, the NFT hype
index was constructed as well as several approaches of ...
Added: December 10, 2023
Dudyrev E., Kuznetsov S., Napoli A., , in: FCA4AI 2023 What can FCA do for Artificial Intelligence 2023 Proceedings of the 11th International Workshop "What can FCA do for Artificial Intelligence?" co-located with the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023) Macao, S.A.R. China; August 20, 2023Vol. 3489.: CEUR-WS.org, 2023. P. 69–80.
Rule Learning and Formal Concept Analysis (FCA) are two fields of science that study similar topic yet speak in a very different terms. This paper describes rule-based machine learning models with FCA-based terminology which results in decision quiver model. A decision quiver, discussed in the paper, is a supervised machine learning model that is based ...
Added: October 4, 2023
Dudyrev E., Kuznetsov S., Napoli A., , in: 17th International Conference, ICFCA 2023, Kassel, Germany, July 17–21, 2023, Proceedings. Formal Concept Analysis, (LNCS, volume 13934).: Switzerland: Springer, 2023. P. 127–142.
In this paper we introduce and study description quivers as compact representations of concept lattices and respective ensembles of decision trees. Formally, description quivers are directed multigraphs where vertices represent concept intents and (multiple) edges represent generators of intents. We study some properties of description quivers and shed light on their use for describing state-of-the-art symbolic machine ...
Added: October 4, 2023
Parakal E. G., Kuznetsov S., , in: Proceedings of the 10th International Workshop "What can FCA do for Artificial Intelligence?"Vol. 3233.: CEUR Workshop Proceedings, 2022. Ch. 2 P. 9–22.
Explanations for the predictions made by Machine Learning (ML) models are best framed in terms of
abstract, high-level concepts that are easily comprehensible to human beings. The use of such concepts
constitutes a subfield of interpretability methods known as concept-based explanations. This work uses
concept-based explanations to build an intrinsically interpretable document classifier using a combination
of Formal Concept ...
Added: May 17, 2023
Ignatov D. I., Kwuida L., Annals of Mathematics and Artificial Intelligence 2022 Vol. 90 No. 11 P. 1197–1222
We propose the usage of two power indices from cooperative game theory and public choice theory for ranking attributes of closed sets, namely intents of formal concepts (or closed itemsets). The introduced indices are related to extensional concept stability and are also based on counting of generators, especially of those that contain a selected attribute. ...
Added: January 31, 2023
Dudyrev E., Kuznetsov S., , in: Proceedings of the 10th International Workshop "What can FCA do for Artificial Intelligence?"Vol. 3233.: CEUR Workshop Proceedings, 2022. P. 23–34.
Studies on Explainable Artificial Intelligence show that a model should be small in order to be human understandable. The restriction on the size of a model drastically reduces the space of possible solutions. Many rule learning models still rely on greedy algorithms for generating ensembles of decision trees. This paper discusses FCA-inspired mathematical and engineering ...
Added: November 1, 2022
Zakharova V., Suvorova A., , in: CEUR Workshop Proceedings. Proceedings of the International Conference "Internet and Modern Society" (IMS-2021), St. Petersburg, 24 - 26 June 2021.: CEUR Workshop Proceedings, 2021. P. 230–234.
Machine Learning (ML) is now widely applied in various life spheres. Experts from different domains become involved in the decision-making on the basis of complex machine learning models that causes in-creased interest in the research in model explainability. However, little is known about the ways that ML-practitioners use to describe and justify their models to others. This work aims to fill the research gap in understanding how data specialists evaluate machine learning models ...
Added: September 28, 2022
Muratova A., Mitrofanova E., Islam R., , in: Procedia Computer Science: 11th International Young Scientist Conference on Computational ScienceVol. 212.: Elsevier, 2022. P. 358–367.
The article presents a case study on demographic sequences analysis through modern machine learning (ML)
techniques. The studied data contains demographic and socioeconomic events, where the events are presented
as sequences of statuses. The involved demographers are interested in applications of advanced ML techniques
and interpretable patterns for their needs. We show how Shapley value-based explanations can be ...
Added: September 10, 2022
Antipov E. A., Pokryshevskaya E. B., Journal of Revenue and Pricing Management 2020 No. 19 P. 355–364
Forecasting demand and understanding sales drivers are one of the most important tasks in retail analytics. However, traditionally, linear models and/or models with a small number of predictors have been predominantly used in sales modeling. Taking into account that real-world demand is naturally determined by complex substitution and complementation patterns among a large number of ...
Added: October 31, 2020
Ignatov D. I., Kwuida L., , in: Ontologies and Concepts in Mind and Machine. 25th International Conference on Conceptual Structures, ICCS 2020.: Springer, 2020. P. 90–102.
Among the family of rule-based classification models, there are classifiers based on conjunctions of binary attributes. For example, the JSM-method of automatic reasoning (named after John Stuart Mill) was formulated as a classification technique in terms of intents of formal concepts as classification hypotheses. These JSM-hypotheses already represent an interpretable model since the respective conjunctions ...
Added: October 30, 2020