# ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning

arXiv: https://arxiv.org/abs/1902.05009 · HTML: https://mlsanity.com/paper/1902.05009/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2019.

## Built on by (most cited first, 23 of 23)

- [πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization](https://mlsanity.com/paper/2204.11051/) (2022)
- [AutoDS: Towards Human-Centered Automation of Data Science](https://mlsanity.com/paper/2101.05273/) (2021)
- [The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations](https://mlsanity.com/paper/2212.11737/) (2022)
- [Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows](https://mlsanity.com/paper/2101.04834/) (2021)
- [Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop](https://mlsanity.com/paper/2101.04296/) (2021)
- [How Much Automation Does a Data Scientist Want?](https://mlsanity.com/paper/2101.03970/) (2021)
- [Position: Towards Bidirectional Human-AI Alignment](https://mlsanity.com/paper/2406.09264/) (2024)
- [Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"](https://mlsanity.com/paper/2302.01448/) (2023)
- [Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML](https://mlsanity.com/paper/2104.04375/) (2021)
- [XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine Learning](https://mlsanity.com/paper/2202.11954/) (2022)
- [AutoML in The Wild: Obstacles, Workarounds, and Expectations](https://mlsanity.com/paper/2302.10827/) (2023)
- [Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling](https://mlsanity.com/paper/2304.08366/) (2023)
- [Characterizing Student Engagement Moods for Dropout Prediction in Question Pool Websites](https://mlsanity.com/paper/2102.00423/) (2021)
- [An Empirical Study on the Usage of Automated Machine Learning Tools](https://mlsanity.com/paper/2208.13116/) (2022)
- [The Role of Hyperparameters in Predictive Multiplicity](https://mlsanity.com/paper/2503.13506/) (2025)
- [AutoSmart: An Efficient and Automatic Machine Learning framework for Temporal Relational Data](https://mlsanity.com/paper/2109.04115/) (2021)
- [Visual Analysis of Neural Architecture Spaces for Summarizing Design Principles](https://mlsanity.com/paper/2208.09665/) (2022)
- [AutoDOViz: Human-Centered Automation for Decision Optimization](https://mlsanity.com/paper/2302.09688/) (2023)
- [AHMoSe: A Knowledge-Based Visual Support System for Selecting Regression Machine Learning Models](https://mlsanity.com/paper/2101.11970/) (2021)
- [DeepCAVE: A Visualization and Analysis Tool for Automated Machine Learning](https://mlsanity.com/paper/2512.01810/) (2025)
- [Practitioner Motives to Use Different Hyperparameter Optimization Methods](https://mlsanity.com/paper/2203.01717/) (2022)
- [Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making](https://mlsanity.com/paper/2403.12664/) (2024)
- [DisCoMBO: Steering Expert-in-the-Loop Black Box Optimization via Distributional Conformance](https://mlsanity.com/paper/2609.36472/) (2026)

## Abstract (arXiv)

To relieve the pain of manually selecting machine learning algorithms and tuning hyperparameters, automated machine learning (AutoML) methods have been developed to automatically search for good models. Due to the huge model search space, it is impossible to try all models. Users tend to distrust automatic results and increase the search budget as much as they can, thereby undermining the efficiency of AutoML. To address these issues, we design and implement ATMSeer, an interactive visualization tool that supports users in refining the search space of AutoML and analyzing the results. To guide the design of ATMSeer, we derive a workflow of using AutoML based on interviews with machine learning experts. A multi-granularity visualization is proposed to enable users to monitor the AutoML process, analyze the searched models, and refine the search space in real time. We demonstrate the utility and usability of ATMSeer through two case studies, expert interviews, and a user study with 13 end users.