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

arXiv: https://arxiv.org/abs/2502.10526 · HTML: https://mlsanity.com/paper/2502.10526/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 93 references that are in the map.
- First posted on arXiv 14 Feb 2025.

## Builds on (most cited first, 30 of 32)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Domino: Discovering Systematic Errors with Cross-Modal Embeddings](https://mlsanity.com/paper/2203.14960/) (2022)
- [Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers](https://mlsanity.com/paper/1801.06889/) (2018)
- [Problem Formulation and Fairness](https://mlsanity.com/paper/1901.02547/) (2019)
- [Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanations](https://mlsanity.com/paper/2210.11584/) (2022)
- [Automated Data Slicing for Model Validation:A Big data - AI Integration Approach](https://mlsanity.com/paper/1807.06068/) (2018)
- [RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records](https://mlsanity.com/paper/1805.10724/) (2018)
- [Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models](https://mlsanity.com/paper/1808.00196/) (2018)
- ["The Human Body is a Black Box": Supporting Clinical Decision-Making with Deep Learning](https://mlsanity.com/paper/1911.08089/) (2019)
- [Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support](https://mlsanity.com/paper/2204.02310/) (2022)
- [Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Care](https://mlsanity.com/paper/2302.00096/) (2023)
- [Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction](https://mlsanity.com/paper/1802.09548/) (2018)
- [Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning](https://mlsanity.com/paper/2302.04732/) (2023)
- [Understanding Frontline Workers' and Unhoused Individuals' Perspectives on AI Used in Homeless Services](https://mlsanity.com/paper/2303.09743/) (2023)
- ["Brilliant AI Doctor" in Rural China: Tensions and Challenges in AI-Powered CDSS Deployment](https://mlsanity.com/paper/2101.01524/) (2021)
- [Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making](https://mlsanity.com/paper/2302.06503/) (2023)
- [Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects](https://mlsanity.com/paper/2002.03389/) (2020)
- [PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines](https://mlsanity.com/paper/2005.00160/) (2020)
- [Sibyl: Understanding and Addressing the Usability Challenges of Machine Learning In High-Stakes Decision Making](https://mlsanity.com/paper/2103.02071/) (2021)
- [Model Sketching: Centering Concepts in Early-Stage Machine Learning Model Design](https://mlsanity.com/paper/2303.02884/) (2023)
- [EXMOS: Explanatory Model Steering Through Multifaceted Explanations and Data Configurations](https://mlsanity.com/paper/2402.00491/) (2024)
- [A User-based Visual Analytics Workflow for Exploratory Model Analysis](https://mlsanity.com/paper/1809.10782/) (2018)
- [Survey on Visual Analysis of Event Sequence Data](https://mlsanity.com/paper/2006.14291/) (2020)
- [Visus: An Interactive System for Automatic Machine Learning Model Building and Curation](https://mlsanity.com/paper/1907.02889/) (2019)
- [Target specification bias, counterfactual prediction, and algorithmic fairness in healthcare](https://mlsanity.com/paper/2308.02081/) (2023)
- [Sketching AI Concepts with Capabilities and Examples: AI Innovation in the Intensive Care Unit](https://mlsanity.com/paper/2402.13437/) (2024)
- [Leveraging Clinical Time-Series Data for Prediction: A Cautionary Tale](https://mlsanity.com/paper/1811.12520/) (2018)

## Built on by (most cited first, 6 of 6)

- [Human-AI Co-design for Clinical Prediction Models](https://mlsanity.com/paper/2601.09072/) (2026)
- [More Than "Means to an End": Supporting Reasoning with Transparently Designed AI Data Science Processes](https://mlsanity.com/paper/2603.24877/) (2026)
- [Measurement as Bricolage: Examining How Data Scientists Construct Target Variables for Predictive Modeling Tasks](https://mlsanity.com/paper/2507.02819/) (2025)
- [Data-Prompt Co-Evolution: Growing Test Sets to Refine LLM Behavior](https://mlsanity.com/paper/2510.12728/) (2025)
- [Who Leads? Comparing Human-Centric and Model-Centric Strategies for Defining ML Target Variables](https://mlsanity.com/paper/2510.25974/) (2025)
- [Intelligent Reasoning Cues: A Framework and Case Study of the Roles of AI Information in Complex Decisions](https://mlsanity.com/paper/2602.00259/) (2026)

## Abstract (arXiv)

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.