Position: A Call to Action for a Human-Centered AutoML Paradigm

MCML Authors

Florian Karl

→ Group Bernd Bischl
 Statistical Learning and Data Science

Julia Moosbauer

Dr.

* Former Member

→ Group Bernd Bischl
 Statistical Learning and Data Science

Matthias Feurer

Prof. Dr.

Thomas Bayes Fellow

* Former Thomas Bayes Fellow

Bernd Bischl

Prof. Dr.

Director

Statistical Learning and Data Science

Abstract

Automated machine learning (AutoML) was formed around the fundamental objectives of automatically and efficiently configuring machine learning (ML) workflows, aiding the research of new ML algorithms, and contributing to the democratization of ML by making it accessible to a broader audience. Over the past decade, commendable achievements in AutoML have primarily focused on optimizing predictive performance. This focused progress, while substantial, raises questions about how well AutoML has met its broader, original goals. In this position paper, we argue that a key to unlocking AutoML’s full potential lies in addressing the currently underexplored aspect of user interaction with AutoML systems, including their diverse roles, expectations, and expertise. We envision a more human-centered approach in future AutoML research, promoting the collaborative design of ML systems that tightly integrates the complementary strengths of human expertise and AutoML methodologies.

inproceedings LKK+24

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ICML 2024

41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024.

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Authors

M. Lindauer • F. Karl • A. Klier • J. Moosbauer • A. Tornede • A. C. Mueller • F. Hutter • M. Feurer • B. Bischl

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Links

URL

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In Collaboration

 Microsoft

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Research Area

 A1 | Statistical Foundations & Explainability