提出可操作的交叉性研究框架，推动AI研究更负责任地对待边缘群体。
QUINTA: Reflexive Sensibility For Responsible AI Research and Data-Driven Processes
- 以研究者自我反思为核心，构建交叉性实践方法论
- 通过#我也是运动案例验证框架有效性，揭示数据流程中的隐性偏见
- 适合关注公平、伦理的AI研究者与数据科学实践者
随着人工智能与机器学习领域日益重视公平性及对历史边缘群体的关注，交叉性在AI研究中的重要性已获得广泛认可。然而，鲜有研究提供将交叉性融入批判性实践的具体指导。本文提出一个基于批判性反思的综合性框架，将交叉性实践系统化地应用于AI/DS（人工智能/数据科学）全流程。引入定量交叉性数据（QUINTA）作为方法论范式，挑战传统且表面化的研究习惯，尤其在数据驱动过程中识别并缓解负面效应，如无意中加剧边缘化现象。该框架聚焦研究者的自我反思，强调研究者在数据驱动的AI/DS成果创建与分析中的权力作用。为验证QUINTA的有效性，本文以#我也是运动为案例，展示了一次反思性的研究者示范。本文于2023年被平等与可及性在算法、机制与优化会议（EAAMO）接收为海报展示。
原文摘要 · Abstract (English)
As the field of artificial intelligence (AI) and machine learning (ML) continues to prioritize fairness and the concern for historically marginalized communities, the importance of intersectionality in AI research has gained significant recognition. However, few studies provide practical guidance on how researchers can effectively incorporate intersectionality into critical praxis. In response, this paper presents a comprehensive framework grounded in critical reflexivity as intersectional praxis. Operationalizing intersectionality within the AI/DS (Artificial Intelligence/Data Science) pipeline, Quantitative Intersectional Data (QUINTA) is introduced as a methodological paradigm that challenges conventional and superficial research habits, particularly in data-centric processes, to identify and mitigate negative impacts such as the inadvertent marginalization caused by these practices. The framework centers researcher reflexivity to call attention to the AI researchers' power in creating and analyzing AI/DS artifacts through data-centric approaches. To illustrate the effectiveness of QUINTA, we provide a reflexive AI/DS researcher demonstration utilizing the \#metoo movement as a case study. Note: This paper was accepted as a poster presentation at Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) Conference in 2023.
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