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

Marius Lindauer, Florian Karl, Anne Klier, Julia Moosbauer, Alexander Tornede, Andreas C Mueller, Frank Hutter, Matthias Feurer, Bernd Bischl
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:30566-30584, 2024.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-lindauer24a, title = {Position: A Call to Action for a Human-Centered {A}uto{ML} Paradigm}, author = {Lindauer, Marius and Karl, Florian and Klier, Anne and Moosbauer, Julia and Tornede, Alexander and Mueller, Andreas C and Hutter, Frank and Feurer, Matthias and Bischl, Bernd}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {30566--30584}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/lindauer24a/lindauer24a.pdf}, url = {https://proceedings.mlr.press/v235/lindauer24a.html}, 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.} }
Endnote
%0 Conference Paper %T Position: A Call to Action for a Human-Centered AutoML Paradigm %A Marius Lindauer %A Florian Karl %A Anne Klier %A Julia Moosbauer %A Alexander Tornede %A Andreas C Mueller %A Frank Hutter %A Matthias Feurer %A Bernd Bischl %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-lindauer24a %I PMLR %P 30566--30584 %U https://proceedings.mlr.press/v235/lindauer24a.html %V 235 %X 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.
APA
Lindauer, M., Karl, F., Klier, A., Moosbauer, J., Tornede, A., Mueller, A.C., Hutter, F., Feurer, M. & Bischl, B.. (2024). Position: A Call to Action for a Human-Centered AutoML Paradigm. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:30566-30584 Available from https://proceedings.mlr.press/v235/lindauer24a.html.

Related Material