Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify Predictive Modeling Tasks
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910836688158720 |
|---|---|
| author | Sivaraman, Venkatesh Vaishampayan, Anika Li, Xiaotong Buck, Brian R Ma, Ziyong Boyce, Richard D Perer, Adam |
| author_facet | Sivaraman, Venkatesh Vaishampayan, Anika Li, Xiaotong Buck, Brian R Ma, Ziyong Boyce, Richard D Perer, Adam |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10526 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify Predictive Modeling Tasks Sivaraman, Venkatesh Vaishampayan, Anika Li, Xiaotong Buck, Brian R Ma, Ziyong Boyce, Richard D Perer, Adam Human-Computer Interaction Artificial Intelligence 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. |
| title | Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify Predictive Modeling Tasks |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2502.10526 |