From Black-Box to Glass-Box: A Review of Explainable Neuro-Symbolic AI for Climate-Induced Food Insecurity Prediction

Adam Omeiza Yusuf, Taiwo Kolajo, Emeka Ogbuju, Francisca Oladipo
Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, PMLR 319:331-345, 2026.

Abstract

We propose a Glass-Box Neuro-Symbolic framework that balances explainability and accuracy for food insecurity prediction in Nigeria. Predictions are grounded in a Knowledge Graph of a harmonised Data Lakehouse, yielding human-readable reasoning paths that relate specific climate anomalies to predicted agricultural outcomes. A user study with agricultural extension officers and policy analysts demonstrates that trust scores for Neuro-Symbolic explanations are significantly higher than SHAP visualisations, supporting the establishment of trusted AI systems for data-driven decisions under climate uncertainty.

Cite this Paper

BibTeX
@InProceedings{pmlr-v319-yusuf26a, title = {From Black-Box to Glass-Box: A Review of Explainable Neuro-Symbolic {AI} for Climate-Induced Food Insecurity Prediction}, author = {Yusuf, Adam Omeiza and Kolajo, Taiwo and Ogbuju, Emeka and Oladipo, Francisca}, booktitle = {Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments}, pages = {331--345}, year = {2026}, editor = {Folorunso, Sakinat and Ogundokun, Roseline and Oladipo, Francisca}, volume = {319}, series = {Proceedings of Machine Learning Research}, month = {11--14 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v319/main/assets/yusuf26a/yusuf26a.pdf}, url = {https://proceedings.mlr.press/v319/yusuf26a.html}, abstract = {We propose a Glass-Box Neuro-Symbolic framework that balances explainability and accuracy for food insecurity prediction in Nigeria. Predictions are grounded in a Knowledge Graph of a harmonised Data Lakehouse, yielding human-readable reasoning paths that relate specific climate anomalies to predicted agricultural outcomes. A user study with agricultural extension officers and policy analysts demonstrates that trust scores for Neuro-Symbolic explanations are significantly higher than SHAP visualisations, supporting the establishment of trusted AI systems for data-driven decisions under climate uncertainty.} }
Endnote
%0 Conference Paper %T From Black-Box to Glass-Box: A Review of Explainable Neuro-Symbolic AI for Climate-Induced Food Insecurity Prediction %A Adam Omeiza Yusuf %A Taiwo Kolajo %A Emeka Ogbuju %A Francisca Oladipo %B Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments %C Proceedings of Machine Learning Research %D 2026 %E Sakinat Folorunso %E Roseline Ogundokun %E Francisca Oladipo %F pmlr-v319-yusuf26a %I PMLR %P 331--345 %U https://proceedings.mlr.press/v319/yusuf26a.html %V 319 %X We propose a Glass-Box Neuro-Symbolic framework that balances explainability and accuracy for food insecurity prediction in Nigeria. Predictions are grounded in a Knowledge Graph of a harmonised Data Lakehouse, yielding human-readable reasoning paths that relate specific climate anomalies to predicted agricultural outcomes. A user study with agricultural extension officers and policy analysts demonstrates that trust scores for Neuro-Symbolic explanations are significantly higher than SHAP visualisations, supporting the establishment of trusted AI systems for data-driven decisions under climate uncertainty.
APA
Yusuf, A.O., Kolajo, T., Ogbuju, E. & Oladipo, F.. (2026). From Black-Box to Glass-Box: A Review of Explainable Neuro-Symbolic AI for Climate-Induced Food Insecurity Prediction. Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, in Proceedings of Machine Learning Research 319:331-345 Available from https://proceedings.mlr.press/v319/yusuf26a.html.

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