Public awareness, trust, and readiness to adopt AI-driven WASH policies

Conceptual framework illustrating the integration of AI in WASH policies from challenges to policy design. The diagram shows that barriers influence public adoption through three key dimensions within the ATR framework: awareness (gap between general AI familiarity and WASH-specific knowledge), trust (including fairness and reliability concerns such as bias, transparency, cybersecurity, and system stability), and readiness (conditional willingness to adopt with preference for human oversight). Data is collected through a mixed-method survey and analyzed using the ATR framework to generate findings, which inform evidence-based policy design, including transparency mechanisms, human-in-the-loop governance, localized AI deployment, regulatory oversight, and capacity building. The integration of artificial intelligence (AI) in water, sanitation, and hygiene (WASH) governance could improve service efficiency, resource management, and public health. However, public knowledge, trust, and perceived benefits are crucial to AI-driven WASH policy implementation. In this study, public awareness of general AI tools versus specialized WASH applications predicted benefits and deep-seated fears, and major adoption determinants are examined. In a mixed-method approach, semistructured survey data were descriptively examined to find patterns and correlations. General AI awareness is comparatively high, but WASH applications are poorly understood. Data privacy, reliability, and lack of human oversight are big issues, yet increased water quality, resource optimization, and faster response times are expected. This study is the first to show that unregulated AI deployment can automate and scale WASH inequalities. The study also introduces and measures AI-specific trust metrics such as Fairness Trust and Reliability Trust beyond attitudinal barriers. The findings show that adoption requires public trust, which is fostered by transparency, government accountability, and strong governance. This research goes beyond theoretical critique to provide policymakers with evidence-based input for building context-appropriate, human-centered, and socially sustainable AI-driven WASH solutions in varied settings like India.