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

TL;DR

Tempo is an interactive system that facilitates collaboration between data scientists and domain experts to specify, prototype, and validate temporal predictive models more transparently and effectively.

Contribution

We introduce Tempo, a novel tool with a temporal query language that enhances collaborative model specification and validation for predictive tasks involving temporal data.

Findings

Tempo enables rapid prototyping of model specifications.

It improves transparency and understanding among stakeholders.

Case studies show faster identification of feasible and promising models.

Abstract

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.