The application of explainable artificial intelligence methods to models for automatic creativity assessment

Ekaterina A. Valueva 1, 2
Ivan Y. Ilyin 3
Тип публикацииJournal Article
Дата публикации2024-10-01
scimago Q2
wos Q1
БС1
SJR0.927
CiteScore7.3
Impact factor4.7
ISSN26248212
Краткое описание
Objective

The study is devoted to comparing various models based on Artificial Intelligence to determine the level of creativity based on drawings performed using the Urban test, as well as analyzing the results of applying explainable artificial intelligence methods to a trained model to identify the most relevant features in drawings that influence the model’s prediction.

Methods

The dataset is represented by a set of 1,823 scanned forms of drawings of participants performed according to the Urban test. The test results of each participant were assessed by an expert. Preprocessed images were used for fine-tuning pre-trained models such as MobileNet, ResNet18, AlexNet, DenseNet, ResNext, EfficientNet, ViT with additional linear layers to predict the participant’s score. Visualization of the areas that are of greatest importance from the point of view of the model was carried out using the Gradient-weighted Class Activation Mapping (Grad-CAM) method.

Results

Trained models based on MobileNet showed the highest prediction accuracy rate of 76%. The results of the application of explainable artificial intelligence demonstrated areas of interest that correlated with the criteria for expert assessment according to the Urban test. Analysis of erroneous predictions of the model in terms of interpretation of areas of interest made it possible to clarify the features of the drawing on which the model relies, contrary to the expert.

Conclusion

The study demonstrated the possibility of using neural network methods for automated diagnosis of the level of creativity according to the Urban test based on the respondents’ drawings. The application of explainable artificial intelligence methods to the trained model demonstrated the compliance of the identified activation zones with the rules of expert assessment according to the Urban test.

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Топ-30

Журналы

1
Experimental Psychology (Russia)
1 публикация, 50%
iMetaOmics
1 публикация, 50%
1

Издатели

1
Moscow State University of Psychology and Education
1 публикация, 50%
Wiley
1 публикация, 50%
1
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ГОСТ |
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Panfilova A. S. et al. The application of explainable artificial intelligence methods to models for automatic creativity assessment // Frontiers in Artificial Intelligence. 2024. Vol. 7.
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Panfilova A. S., Valueva E. A., Ilyin I. Y. The application of explainable artificial intelligence methods to models for automatic creativity assessment // Frontiers in Artificial Intelligence. 2024. Vol. 7.
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TY - JOUR
DO - 10.3389/frai.2024.1310518
UR - https://www.frontiersin.org/articles/10.3389/frai.2024.1310518/full
TI - The application of explainable artificial intelligence methods to models for automatic creativity assessment
T2 - Frontiers in Artificial Intelligence
AU - Panfilova, Anastasia S.
AU - Valueva, Ekaterina A.
AU - Ilyin, Ivan Y.
PY - 2024
DA - 2024/10/01
PB - Frontiers Media S.A.
VL - 7
PMID - 39411346
SN - 2624-8212
ER -
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BibTex (до 50 авторов) Скопировать
@article{2024_Panfilova,
author = {Anastasia S. Panfilova and Ekaterina A. Valueva and Ivan Y. Ilyin},
title = {The application of explainable artificial intelligence methods to models for automatic creativity assessment},
journal = {Frontiers in Artificial Intelligence},
year = {2024},
volume = {7},
publisher = {Frontiers Media S.A.},
month = {oct},
url = {https://www.frontiersin.org/articles/10.3389/frai.2024.1310518/full},
doi = {10.3389/frai.2024.1310518}
}