Speaker
Description
In the context of the ESS Innovation Agenda, quality assurance must evolve to meet the challenges posed by new technologies and data sources. This contribution reflects on three challenges shaping that evolution: the integration of Artificial Intelligence (AI) and Machine Learning in statistical processes, the operationalisation of Statistics under Development (SuD), and the growing reliance on non-traditional sources like privately held data (PHD). These challenges call for a shift towards more dynamic and context-dependent approaches to quality.
Rather than applying a fixed model, emerging practices focus on tailoring quality requirements to specific use cases and development stages. Across these areas, we see frameworks that allow for gradual alignment with official quality standards, incorporate quality-by-design principles, and support innovation while maintaining trust. Within this transformation, the European Statistics Code of Practice (CoP) and the more operational Quality Assurance Framework (QAF) play an important role.
While the CoP structure continues to provide a stable high-level compass, its application increasingly calls for interpretations, flexible implementation, and targeted extensions—particularly when working with AI or with complex processes involving external actors such as private data holders. The presentation will highlight common threads across these initiatives and illustrate how standards, principles and architectures can support innovation without compromising on the fundamental values of trust, transparency, and accountability. Examples will be drawn from recent ESS work on AI strategy and governance, quality profiling for SuD, and reproducible workflows for one particular type of PHD data, namely Mobile Network Operator data.
