Towards Model-Driven Explainable Artificial Intelligence. An Experiment with Shallow Methods Versus Grammatical Evolution

Sepioło, Dominik; Ligęza, Antoni

doi:10.1007/978-3-031-50485-3_36

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2024

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Cited by 2 publications

References 7 publications

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In Search for Model-Driven eXplainable Artificial Intelligence

Ligęza,

Sepioło

2024

IFIP Advances in Information and Communication Technology

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In Search for Model-Driven eXplainable Artificial Intelligence

Ligęza,

Sepioło

2024

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

Towards Model-Driven Explainable Artificial Intelligence: Function Identification with Grammatical Evolution

Sepioło,

Ligęza

2024

Applied Sciences

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Machine learning is a well-matured discipline, and exploration of datasets can be performed in an efficient way, leading to accurate and operational prediction and decision models. On the other hand, most methods tend to produce black-box-type models, which can be considered a serious drawback. This is so, since in case of numerous practical applications, it is also required to justify, explain, and uncover the inner decision mechanism so that an in-depth understanding of the causal and functional dependencies becomes possible and some responsibility for the decision can be considered. This paper addresses the critical need for model-driven eXplainable Artificial Intelligence (XAI) by exploring the limitations inherent in existing explanatory mechanisms, such as LIME or SHAP, which rely solely on input data. This seems to be an intrinsic limitation and a conceptual error, as no expert domain knowledge can come into play, and no analytical models of the phenomena under investigation are created. In order to deal with this issue, this paper puts forward the idea of building open, white-box explanatory models. To do that, we propose employing grammatical evolution tools combined with expert domain knowledge. The results demonstrate that the developed models can effectively explain the structure and behavior of decision models in terms of components, connections, causality, and simple functional dependencies.

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Towards Model-Driven Explainable Artificial Intelligence. An Experiment with Shallow Methods Versus Grammatical Evolution

Cited by 2 publications

References 7 publications

In Search for Model-Driven eXplainable Artificial Intelligence

In Search for Model-Driven eXplainable Artificial Intelligence

Towards Model-Driven Explainable Artificial Intelligence: Function Identification with Grammatical Evolution

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