Authors - Anna Berko-Boateng, Chalisa Veesommai Sillberg, Mika Saari, Pekka Abrahamsson Abstract - Electronic waste (e-waste) is one of the fastest-growing waste streams worldwide, and in many low-resource settings, informal recycling is performed under hazardous conditions with limited access to occupational safety information. Existing AI-based waste-recognition systems are typically designed for industrial or high-resource environments and do not adequately address the infrastructural, usability, and safety constraints of informal work contexts. To address this gap, this paper presents a lightweight Android application that uses multimodal artificial intelligence (AI) to support occupational safety among informal e-waste workers. The application enables users to capture images of e-waste components and receive structured safety guidance, including risk levels and handling instructions. Through the design, implementation, and field evaluation of the system, five meta-requirements were derived for AI-supported safety tools operating in low-resource environments. The system was evaluated through field testing at two informal recycling sites in Accra, Ghana, using representative low-cost smartphones. The findings indicate that the participants perceived the guidance as relevant and useful, while the interaction flow operated reliably across heterogeneous devices and user backgrounds. Beyond demonstrating feasibility, the study contributes transferable design knowledge on AI integration, device constraints, and safety-critical communication in low-resource socio-technical contexts.
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