首次将地理人工智能偏见与欧盟人工智能法案对接，明确高风险系统审计要求。
From Bias to Accountability: How the EU AI Act Confronts Challenges in European GeoAI Auditing
- 梳理地理AI偏见机制并对应欧盟法案条款，建立审计映射框架。
- 证明主流地理AI应用符合法案高风险标准，需在2027年前开展审计。
- 提供可操作的偏见检测方法，适合政策制定者与技术开发者参考。
地理空间人工智能（GeoAI）模型中的偏见已有记录，但证据分散于各类专项研究中。本文整合零散文献，系统梳理了GeoAI中的偏见机制，包括代表性偏见、算法偏见和聚合偏见，并将其映射至欧盟《人工智能法案》（EU AI Act）的具体条款。通过应用法案的高风险判定标准，我们证明广泛部署的GeoAI应用属于高风险系统。文章还呈现了近期审计案例，并提出实用的偏见检测方法。据我们所知，这是首个将GeoAI偏见证据融入欧盟人工智能法案语境的研究，首次识别出高风险GeoAI系统，并实现偏见机制与法案条文的精准对应。尽管分析具有探索性，但结果表明，即使经过良好筛选的欧洲数据集也应在2027年法案全面实施前，开展常规偏见审计。
原文摘要 · Abstract (English)
Bias in geospatial artificial intelligence (GeoAI) models has been documented, yet the evidence is scattered across narrowly focused studies. We synthesize this fragmented literature to provide a concise overview of bias in GeoAI and examine how the EU's Artificial Intelligence Act (EU AI Act) shapes audit obligations. We discuss recurring bias mechanisms, including representation, algorithmic and aggregation bias, and map them to specific provisions of the EU AI Act. By applying the Act's high-risk criteria, we demonstrate that widely deployed GeoAI applications qualify as high-risk systems. We then present examples of recent audits along with an outline of practical methods for detecting bias. As far as we know, this study represents the first integration of GeoAI bias evidence into the EU AI Act context, by identifying high-risk GeoAI systems and mapping bias mechanisms to the Act's Articles. Although the analysis is exploratory, it suggests that even well-curated European datasets should employ routine bias audits before 2027, when the AI Act's high-risk provisions take full effect.
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