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Authors: Imane El Assari 1 ; Hajar Hakkoum 1 and Ali Idri 1 ; 2

Affiliations: 1 ENSIAS, Mohammed V University in Rabat, Morocco ; 2 AlKhwarizmi, Mohammed VI Polytechnic University, Benguerir, Morocco

Keyword(s): Interpretability, xAI, Biodiversity, Species Distribution Modelling, PDP, SHAP, Machine Learning.

Abstract: Species Distribution models (SDMs) are widely used to study species occurrence in conservation science and ecology evolution. However the huge amount of data and its complexity makes it difficult for professionals to forecast the evolutionary trends of distributions across the concerned landscapes. As a solution, machine learning (ML) algorithms were used to construct and evaluate SDMs in order to predict the studied species occurrences and their habitat suitability. Nevertheless, it is critical to ensure that ML based SDMs reflect reality by studying their trustworthiness. This paper aims to investigate two techniques: SHapley Additive exPlanations (SHAP) and the Partial Dependence Plot (PDP) techniques to interpret a Multilayer perceptron (MLP) trained on the Loxodonta Africana dataset. Results demonstrate the prediction process and how in- terpretability techniques could be used to explain misclassified instances and thus increase trust between ML results and domain experts.

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Paper citation in several formats:
El Assari, I.; Hakkoum, H. and Idri, A. (2023). Explainability of MLP Based Species Distribution Models: A Case Study. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-623-1; ISSN 2184-433X, SciTePress, pages 690-697. DOI: 10.5220/0011745300003393

@conference{icaart23,
author={Imane {El Assari}. and Hajar Hakkoum. and Ali Idri.},
title={Explainability of MLP Based Species Distribution Models: A Case Study},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2023},
pages={690-697},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011745300003393},
isbn={978-989-758-623-1},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Explainability of MLP Based Species Distribution Models: A Case Study
SN - 978-989-758-623-1
IS - 2184-433X
AU - El Assari, I.
AU - Hakkoum, H.
AU - Idri, A.
PY - 2023
SP - 690
EP - 697
DO - 10.5220/0011745300003393
PB - SciTePress

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