# Towards Human Centered AutoML

arXiv: https://arxiv.org/abs/1911.02391 · HTML: https://mlsanity.com/paper/1911.02391/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2019.

## Built on by (most cited first, 5 of 5)

- [Explaining Hyperparameter Optimization via Partial Dependence Plots](https://mlsanity.com/paper/2111.04820/) (2021)
- [AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks](https://mlsanity.com/paper/2306.08107/) (2023)
- [Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning](https://mlsanity.com/paper/2309.03581/) (2023)
- [CliMB: An AI-enabled Partner for Clinical Predictive Modeling](https://mlsanity.com/paper/2410.03736/) (2024)
- [Automatic Componentwise Boosting: An Interpretable AutoML System](https://mlsanity.com/paper/2109.05583/) (2021)

## Abstract (arXiv)

Building models from data is an integral part of the majority of data science workflows. While data scientists are often forced to spend the majority of the time available for a given project on data cleaning and exploratory analysis, the time available to practitioners to build actual models from data is often rather short due to time constraints for a given project. AutoML systems are currently rising in popularity, as they can build powerful models without human oversight. In this position paper, we aim to discuss the impact of the rising popularity of such systems and how a user-centered interface for such systems could look like. More importantly, we also want to point out features that are currently missing in those systems and start to explore better usability of such systems from a data-scientists perspective.