H-VALIDA: A Hybrid Methodological Framework for Transparent AI-Assisted Surveys in Data-Scarce Contexts: A Conceptual Framework for Responsible AI Integration
Abstract
Survey research has taken up artificial intelligence quickly, largely because of speed, scale, and lower cost. Those same gains bring methodological risks that become especially serious where data are thin, several languages are in use, or cultural meaning is tightly layered. This article puts forward H-VALIDA (Hybrid Validation and Auditing for Localized, Interpretable, Documented, and Accountable AI-Assisted Surveys) as both a conceptual and an operational framework for AI-assisted surveys that are more reliable, more sensitive to context, and more firmly grounded in ethics. The framework draws together critical data studies, human-centred AI, the total survey error tradition, classical survey methodology, and decolonial principles of data governance. Seven interlocking pillars organise the work: Hybrid Human-AI Collaboration, Contextual Sampling, Algorithmic Auditing, Human Validation, Source Triangulation, Open Documentation, and Ethical Accountability. Those pillars are mapped onto an expanded total survey error taxonomy, and the CARE Principles for Indigenous data governance run through the design as a whole. The article is a methodological design study rather than a field experiment. Development of the framework followed a five-stage protocol: a diagnostic review of documented weaknesses in AI-assisted surveys; a structured synthesis of the literature; integration of principles; construction of the pillars; and operationalisation. Prospective appraisal rested on a specified heuristic rubric, a worked multilingual scenario, and a comparison with a purely automated baseline. No expert-panel scores, statistical tests, or national field implementation are reported here. Claims of improvement are therefore stated as theoretically grounded and operationally specified propositions that still require empirical confirmation. The central argument is that AI systems function as sociotechnical arrangements rather than as neutral instruments, and that validity depends on continuous human oversight, an open record of process, and deliberate adjustment to local conditions.
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