Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify Predictive Modeling Tasks

要旨

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.

著者
Venkatesh Sivaraman
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Anika Vaishampayan
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Xiaotong Li
University of Pittsburgh, Pittsburgh, Pennsylvania, United States
Brian R. Buck
University of Pittsburgh, Pittsburgh, Pennsylvania, United States
Ziyong Ma
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Richard D. Boyce
University of Pittsburgh, Pittsburgh, Pennsylvania, United States
Adam Perer
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
DOI

10.1145/3706598.3713664

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713664

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Personal Data and Decision-Making

Annex Hall F204
6 件の発表
2025-04-28 20:10:00
2025-04-28 21:40:00
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