Explainable AI Framework for Optimizing Urban Waste Management Toward Sustainable Smart Cities

Dwi Apriliasari; Qurotul Aini; Nesti Anggraini Santoso; Alessandro Daeli Garcia

doi:10.34306/ajri.v8i1.1647

Authors

Dwi Apriliasari Pandawan Group https://orcid.org/0000-0001-5597-0475
Qurotul Aini Satya Wacana Christian University https://orcid.org/0000-0002-7546-5721
Nesti Anggraini Santoso Alfabet Inkubator Indonesia https://orcid.org/0009-0005-4568-9715
Alessandro Daeli Garcia Eduaward Incoporation https://orcid.org/0009-0004-9581-9357

DOI:

https://doi.org/10.34306/ajri.v8i1.1647

Keywords:

Artificial Intelligence, Digital Banking, Machine Learning, Financial Fraud Detection, Systematic Literature Review

Abstract

Rapid urbanization and increasing waste generation have created significant challenges for urban waste management, particularly in improving collection efficiency, waste classification, recycling, and environmental monitoring. Al though Artificial Intelligence (AI) offers promising solutions, the lack of transparency in AI-driven decisions may reduce stakeholder trust and limit its broader implementation. This study aims to develop an Explainable Artificial Intelligence (XAI) framework for optimizing urban waste management and supporting environmental resilience and sustainable smart cities. This study employs a conceptual framework development approach by integrating AI-based waste prediction, automated waste classification, collection route optimization, and environmental monitoring with explainability mechanisms. The framework emphasizes transparent and interpretable AI-generated recommendations for supporting stakeholder decision-making. The proposed framework indicates that XAI can improve operational efficiency, optimize resource allocation, support recycling processes, and enhance the transparency of waste management decisions. Explainable insights also enable governments, waste management operators, and communities to better understand and evaluate AI-based recommendations. Integrating XAI into urban waste management can create a more intelligent, transparent, and adaptive system. The proposed framework contributes to environmental resilience and supports the transition toward sustainable and data-driven smart cities.

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