让数据科学家与领域专家共同定义预测模型的使用时机和对象。
Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify Predictive Modeling Tasks
- 用简洁的时序查询语言让模型规范更透明可改。
- 领域专家可检查子群体表现，验证模型是否符合预期。
- 适合医疗、公共政策等需要跨领域协作的预测项目。
时间预测模型在医疗、公共服务等领域有潜力提升决策质量，但常因模型规范不当而未能有效支持决策者。已有研究表明，模型行为与决策者预期之间的偏差多源于模型规范问题，即预测何时、为谁、如何生成。然而，现有预测任务的规范高度技术化，非数据科学人员难以理解与评估。为此，我们开发了Tempo——一个交互式系统，助力数据科学家与领域专家协同优化模型规范。通过Tempo提供的简单而精确的时序查询语言，数据科学家可快速原型化规范，并清晰展示预处理选择；领域专家则能评估各数据子群体的表现，验证模型行为是否符合预期。通过三个案例研究，我们证明Tempo能帮助跨学科团队快速排除不可行方案，聚焦更有前景的方向探索。
原文摘要 · Abstract (English)
Temporal predictive models have the potential to improve decisions in health care, public services, and other domains, yet they often fail to effectively support decision-makers. Prior literature shows that many misalignments between model behavior and decision-makers' expectations stem from issues of model specification, namely how, when, and for whom predictions are made. However, model specifications for predictive tasks are highly technical and difficult for non-data-scientist stakeholders to interpret and critique. To address this challenge we developed Tempo, an interactive system that helps data scientists and domain experts collaboratively iterate on model specifications. Using Tempo's simple yet precise temporal query language, data scientists can quickly prototype specifications with greater transparency about pre-processing choices. Moreover, domain experts can assess performance within data subgroups to validate that models behave as expected. Through three case studies, we demonstrate how Tempo helps multidisciplinary teams quickly prune infeasible specifications and identify more promising directions to explore.
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