用共创方式构建公共空间评估数据集，避免偏见。
AI-EDI-SPACE: A Co-designed Dataset for Evaluating the Quality of Public Spaces
- 让多方利益相关者参与数据设计，融入公平多元包容原则。
- 基于街景图像构建数据集，有效捕捉不同人群对公共空间的评价差异。
- 适合关注社会公平与AI伦理的研究者或城市规划从业者。
人工智能的发展依赖于大规模、精心标注的数据集，但众包平台在数据采集过程中常存在透明度不足、代表性缺失的问题。低薪工人参与导致数据质量受限，且难以反映多样视角，进而使算法加剧对特定群体的偏见。为此，本文提出一种融合公平性、多样性与包容性（EDI）原则的共创方法，让利益相关方在关键阶段深度参与。基于该方法，我们构建了一个用于评估公共空间质量的街景图像数据集及配套模型，验证了其在捕捉多元观点、提升数据质量方面的有效性。
原文摘要 · Abstract (English)
Advancements in AI heavily rely on large-scale datasets meticulously curated and annotated for training. However, concerns persist regarding the transparency and context of data collection methodologies, especially when sourced through crowdsourcing platforms. Crowdsourcing often employs low-wage workers with poor working conditions and lacks consideration for the representativeness of annotators, leading to algorithms that fail to represent diverse views and perpetuate biases against certain groups. To address these limitations, we propose a methodology involving a co-design model that actively engages stakeholders at key stages, integrating principles of Equity, Diversity, and Inclusion (EDI) to ensure diverse viewpoints. We apply this methodology to develop a dataset and AI model for evaluating public space quality using street view images, demonstrating its effectiveness in capturing diverse perspectives and fostering higher-quality data.
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