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Interactive and Explainable Human-Centered AutoML

CORDIS · observation · Publication date unknown

Interactive and Explainable Human-Centered AutoML Trust and interactivity are key factors in the future development and use of automated machine learning (AutoML), supporting developers and researchers in determining powerful task-specific machine learning pipelines, including pre-processing, predictive algorithm, their hyperparameters and--if applicable--the architecture design of deep neural networks. Although AutoML is ready for its prime time after it achieved impressive results in several machine learning (ML) applications and its efficiency improved by several orders of magnitudes in recent years, democratization of machine learning via AutoML is still not achieved. In contrast to previously purely automation-centered approaches, ixAutoML is designed with human users at its heart in several stages. First of all, the foundation of trustful use of AutoML will be based on explanations of its results and processes. Therefore, we aim for: 1. Explaining static effects of design decisions in ML pipelines optimized by stat

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recordType

award

status

SIGNED

region

EU

value

1459763

unit

EUR