Data Quality for Responsible AI in Energy Systems (DARES) investigates the relationship between energy systems, data and AI, focusing on the challenges of data quality for responsible AI in the green transition. While powerful AI systems can help achieve ambitious carbon emission reduction goals, lack of data quality is one core reason for AI failure, risking blackouts and other energy system failures. For the Nordic Region to become the most sustainable and integrated region in the world, the problem of data quality must be addressed.

The challenge of data quality is a world-wide concern. It is clear that technical approaches are not enough as definitions of data quality have not changed for nearly 30 years and top scholars in data management agree that existing definitions remain vague and systematic research on assessing data quality is largely absent. DARES is a beginning - a project that brings together a strong interdisciplinary team to build a Nordic- Baltic scholarly community around data quality, increasing regional expertise and building critical mass necessary to push the idea of socio-technical approaches to data quality world-wide. Leveraging a diverse, interdisciplinary, multi-country consortium, DARES aims to deliver:

1. Comprehensive policy recommendations for data quality in responsible and sustainable AI development to support Nordic-Baltic green transition goals
2. Well-defined guidelines for data quality goals in data generation processes, AI system development pipelines, and AI system life-cycle for energy industries
3. Software libraries, benchmarks, toolkits, and evaluation approaches for incorporating data quality in synthetic data development and quality aware machine learning approaches
4. Energy futures workshops for industry and policy makers engaging shared Nordic- Baltic values