Abstract

Machine learning (ML) reproducibility is often framed as a problem of incomplete artifact recording. This framing leads to systems that prioritize capturing datasets, code, configurations, and execution environments. However, in collaborative and interdisciplinary ML projects, reproducibility failures often arise not only from missing artifacts but from difficulties in interpreting prior work, aligning evolving components, and reconstructing experimental intent over time. Drawing on a 19-month deployment of a data-centric ML management system in a clinical research project, we identify recurring interactional breakdowns that persist despite comprehensive structural traceability. Based on these findings, we propose a two-layer socio-technical ML management system combining lifecycle-aware artifact infrastructure with an interactional layer designed to mediate coordination, explanation, and shared understanding. We …

Date

2026

Authors

Zhiwei Li, Carl Kesselman

Book

Proceedings of the Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems

Pages

1-5