用行为科学改写数据科学伦理，让从业者自觉避免偏见。
Behavior Matters: An Alternative Perspective on Promoting Responsible Data Science
- 结合认知心理学与数据流程，设计行为干预机制
- 提出可落地的机器学习与可视化分析干预方案
- 适合关注数据伦理实践的研究者与从业者
数据科学流程影响着从购物选择到就业、居住等日常决策。若设计不当，极易加剧社会不公。传统方法多聚焦技术层面（如缓解算法偏见）。本文提出新视角：融合认知心理学的行为改变理论与数据科学工作流知识及伦理规范，构建负责任数据科学的新范式。通过实例展示在机器学习与可视化数据分析中可实施的行为干预，旨在阻断不良或疏忽实践，强化伦理行为。最后呼吁学界探索行为干预在负责任数据科学中的研究潜力。
原文摘要 · Abstract (English)
Data science pipelines inform and influence many daily decisions, from what we buy to who we work for and even where we live. When designed incorrectly, these pipelines can easily propagate social inequity and harm. Traditional solutions are technical in nature; e.g., mitigating biased algorithms. In this vision paper, we introduce a novel lens for promoting responsible data science using theories of behavior change that emphasize not only technical solutions but also the behavioral responsibility of practitioners. By integrating behavior change theories from cognitive psychology with data science workflow knowledge and ethics guidelines, we present a new perspective on responsible data science. We present example data science interventions in machine learning and visual data analysis, contextualized in behavior change theories that could be implemented to interrupt and redirect potentially suboptimal or negligent practices while reinforcing ethically conscious behaviors. We conclude with a call to action to our community to explore this new research area of behavior change interventions for responsible data science.
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