Small Data Explainer -- The impact of small data methods in everyday life
It addresses the problem of leveraging limited data for societal and health applications, but is incremental as it synthesizes existing methods without introducing new paradigms.
The paper tackles the challenge of applying AI in small data settings, such as underrepresented groups in policy or health wearables, by providing a conceptual overview and technical solutions from statistics and computer science, but does not report concrete numerical results.
The emergence of breakthrough artificial intelligence (AI) techniques has led to a renewed focus on how small data settings, i.e., settings with limited information, can benefit from such developments. This includes societal issues such as how best to include under-represented groups in data-driven policy and decision making, or the health benefits of assistive technologies such as wearables. We provide a conceptual overview, in particular contrasting small data with big data, and identify common themes from exemplary case studies and application areas. Potential solutions are described in a more detailed technical overview of current data analysis and modelling techniques, highlighting contributions from different disciplines, such as knowledge-driven modelling from statistics and data-driven modelling from computer science. By linking application settings, conceptual contributions and specific techniques, we highlight what is already feasible and suggest what an agenda for fully leveraging small data might look like.