Machine learning (ML) is a driving force for many successful applications in Artificial Intelligence. ML pipelines ensure guarantees on the entirety of the system (i.e., horizontal certification) as well as on each single component (i.e., vertical certification). The horizontal certification covers the full pipeline from data acquisition to data visualization. Moreover, it spans over user-centered, technical, financial, and regulatory aspects of the system. The vertical certification exploits the theory of ML to guarantee error bounds, sampling complexity, energy consumption, execution time, time-to-think, and memory and communication demands. The understandability of an ML pipeline in its entirety requires the collaboration of researchers from the database and the ML communities.
ETMLP workshop will examine the aforementioned opportunities and their associated challenges. The main objective of this workshop is to create a forum where researchers from machine learning, data management, and practitioners engage with ideas around explainability and certified trustworthiness of ML pipelines, at the pipeline level, as well as the component level.
The ultimate goal of the workshop is to discuss recommendations for further work in science and industry and society regarding explainable ML pipelines.
Important dates
Workshop date: 30 March 2020
Submissions: 20 December 2019 12 January 2020, 11:59PM CET
Notification of outcome: 31 January 2020, 11:59PM CET (before EDBT 2020 early registration deadline)
Camera ready due: 4 February 2020, 11:59PM CET
