探索小数据如何在政策与医疗中提升公平性与实用性
Small Data Explainer -- The impact of small data methods in everyday life
- 对比小数据与大数据关系，梳理跨学科方法框架
- 通过案例揭示小数据在弱势群体赋能中的实际价值
- 适合关注公平AI与资源受限场景的研究者参考
突破性人工智能技术的兴起，重新引发对小数据环境（即信息有限情境）如何受益于这些进展的关注。这涉及社会议题，如如何在数据驱动的政策与决策中更好纳入代表性不足群体，或辅助技术在健康领域的潜在益处。本文提供概念性综述，厘清小数据与大数据的关系，并从典型案例与应用领域中提炼共性主题。通过更详细的技朮概述，介绍当前数据分析与建模方法，突出统计学中的知识驱动建模与计算机科学中的数据驱动建模的贡献。通过连接应用场景、概念贡献与具体技术，明确现有可行方案，并提出全面利用小数据的未来研究议程。
原文摘要 · Abstract (English)
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. We provide a conceptual overview, clarify the relationship between small data and 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.
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