2021/10/24 by Jacopo Tagliabue, Tagliabue, Jacopo, Ville Tuulos +5 · 1 citation
Computer Science · Decision Sciences · #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Data Classification #Scientific Computing and Data Management #Software Engineering (cs.SE)
paper · pdf · doi:10.48550/arxiv.2110.13601
openalex publication_date 2021/10/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
With the progressive commoditization of modeling capabilities, data-centric AI recognizes that what happens before and after training becomes crucial for real-world deployments. Following the intuition behind Model Cards, we propose DAG Cards as a form of documentation encompassing the tenets of a data-centric point of view. We argue that Machine Learning pipelines (rather than models) are the most appropriate level of documentation for many practical use cases, and we share with the community an open implementation to generate cards from code.