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

TL;DR

This paper advocates for a shift in AutoML research towards a human-centered paradigm, emphasizing user interaction, diverse roles, and collaboration between human expertise and automated systems to fulfill AutoML's broader goals.

Contribution

It highlights the importance of human-centered design in AutoML and calls for future research to focus on user interaction and collaboration.

Findings

AutoML has mainly optimized predictive performance so far.

Addressing user interaction is key to unlocking AutoML's full potential.

A human-centered approach can enhance AutoML's effectiveness and accessibility.

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.