Interactive and Explainable Human-Centered AutoML

Introduction

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 algorithms, 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.

Human-Centered Design

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 state-of-the-art AutoML systems.
2. Explaining dynamic AutoML policies for temporal aspects of dynamically adapted hyperparameters while ML models are trained.

Interaction and Efficiency

These explanations will be the base for allowing interactions, bringing the best of two worlds together: human intuition and generalization capabilities for complex systems, and the efficiency of systematic optimization approaches for AutoML. Concretely, we aim for:

3. Enabling interactions between humans and AutoML by taking human's latent knowledge into account and learning when to interact.
4. Building first ixAutoML prototypes and showing its efficiency in the context of Industry 4.0.

Conclusion

Perfectly aligned with the EU's AI strategy and recent efforts on interpretability in the ML community, we strongly believe that this timely human-centered ixAutoML will have a substantial impact on the democratization of machine learning.