AI explainability framework for environmental management research.
J Environ Manage
; 342: 118149, 2023 Sep 15.
Article
in En
| MEDLINE
| ID: mdl-37187074
ABSTRACT
Deep learning networks powered by AI are essential predictive tools relying on image data availability and processing hardware advancements. However, little attention has been paid to explainable AI (XAI) in application fields, including environmental management. This study develops an explainability framework with a triadic structure to focus on input, AI model and output. The framework provides three main contributions. (1) A context-based augmentation of input data to maximize generalizability and minimize overfitting. (2) A direct monitoring of AI model layers and parameters to use leaner (lighter) networks suitable for edge device deployment, (3) An output explanation procedure focusing on interpretability and robustness of predictive decisions by AI networks. These contributions significantly advance state of the art in XAI for environmental management research, offering implications for improved understanding and utilization of AI networks in this field.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Conservation of Natural Resources
/
Deep Learning
Type of study:
Prognostic_studies
Language:
En
Journal:
J Environ Manage
Year:
2023
Document type:
Article