H-VALIDA: A Hybrid Methodology for Transparent and Validated AI-Assisted Surveys in Data-Scarce Contexts - A Methodological Framework for Responsible AI Integration

Abstract

Artificial intelligence has moved rapidly into survey research, bringing clear practical gains alongside serious methodological dangers. These dangers are especially sharp in settings that lack abundant data, that operate across multiple languages, or that contain layered cultural complexity. This article presents H-VALIDA (Hybrid Validation and Auditing for Localized, Interpretable, Documented, and Accountable AI-Assisted Surveys). The framework is a transparent heuristic approach intended to raise the reliability, contextual fit, and ethical standing of AI-assisted surveys. It draws on critical data studies, human-centred AI, the total survey error tradition, established survey methodology, and decolonial lines of thought. Seven interdependent pillars structure the approach: Hybrid Human-AI Collaboration, Contextual Sampling, Algorithmic Auditing, Human Validation, Source Triangulation, Open Documentation, and Ethical Accountability. The pillars are mapped onto an expanded total survey error taxonomy, and the CARE Principles for Indigenous data governance are integrated throughout. The central claim is that AI systems function as sociotechnical arrangements rather than neutral tools. Methodological validity therefore depends on sustained human oversight, open documentation, and deliberate adaptation to local conditions. H-VALIDA supplies institutions with a workable and theoretically grounded model for using computational power without surrendering interpretability, fairness, or public confidence.