# Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop

arXiv: https://arxiv.org/abs/2101.04296 · HTML: https://mlsanity.com/paper/2101.04296/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 50 references that are in the map.
- First posted on arXiv 12 Jan 2021.

## Builds on (most cited first, 15 of 15)

- [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI](https://mlsanity.com/paper/1909.02309/) (2019)
- [Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science](https://mlsanity.com/paper/1603.06212/) (2016)
- [Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs](https://mlsanity.com/paper/2004.11440/) (2020)
- [How do Data Science Workers Collaborate? Roles, Workflows, and Tools](https://mlsanity.com/paper/2001.06684/) (2020)
- [Automated Machine Learning: From Principles to Practices](https://mlsanity.com/paper/1810.13306/) (2018)
- [A Survey of Visual Analytics Techniques for Machine Learning](https://mlsanity.com/paper/2008.09632/) (2020)
- [Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems](https://mlsanity.com/paper/2001.06509/) (2020)
- [AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinates](https://mlsanity.com/paper/1912.06723/) (2019)
- [ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning](https://mlsanity.com/paper/1902.05009/) (2019)
- [Soliciting Human-in-the-Loop User Feedback for Interactive Machine Learning Reduces User Trust and Impressions of Model Accuracy](https://mlsanity.com/paper/2008.12735/) (2020)
- [PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines](https://mlsanity.com/paper/2005.00160/) (2020)
- [Surfacing Visualization Mirages](https://mlsanity.com/paper/2001.02316/) (2020)
- [Data Science: A Comprehensive Overview](https://mlsanity.com/paper/2007.03606/) (2020)

## Built on by (most cited first, 21 of 21)

- [Interactive Model Cards: A Human-Centered Approach to Model Documentation](https://mlsanity.com/paper/2205.02894/) (2022)
- [Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML](https://mlsanity.com/paper/2303.08485/) (2023)
- [Automated data processing and feature engineering for deep learning and big data applications: a survey](https://mlsanity.com/paper/2403.11395/) (2024)
- [Position: A Call to Action for a Human-Centered AutoML Paradigm](https://mlsanity.com/paper/2406.03348/) (2024)
- [Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration](https://mlsanity.com/paper/2309.15723/) (2023)
- [Naive Automated Machine Learning](https://mlsanity.com/paper/2111.14514/) (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)
- [An Empirical Study on the Usage of Automated Machine Learning Tools](https://mlsanity.com/paper/2208.13116/) (2022)
- [Assessing the Use of AutoML for Data-Driven Software Engineering](https://mlsanity.com/paper/2307.10774/) (2023)
- [Naive Automated Machine Learning -- A Late Baseline for AutoML](https://mlsanity.com/paper/2103.10496/) (2021)
- [Practitioner Motives to Use Different Hyperparameter Optimization Methods](https://mlsanity.com/paper/2203.01717/) (2022)
- [DREAM: Debugging and Repairing AutoML Pipelines](https://mlsanity.com/paper/2401.00379/) (2023)
- [Data-Prompt Co-Evolution: Growing Test Sets to Refine LLM Behavior](https://mlsanity.com/paper/2510.12728/) (2025)
- [Man versus Machine: AutoML and Human Experts' Role in Phishing Detection](https://mlsanity.com/paper/2108.12193/) (2021)
- [Eliciting Model Steering Interactions from Users via Data and Visual Design Probes](https://mlsanity.com/paper/2310.09314/) (2023)
- [Human-Centered AI Product Prototyping with No-Code AutoML: Conceptual Framework, Potentials and Limitations](https://mlsanity.com/paper/2402.07933/) (2024)
- [Behavior Matters: An Alternative Perspective on Promoting Responsible Data Science](https://mlsanity.com/paper/2410.17273/) (2024)
- [Medical artificial intelligence toolbox (MAIT): an explainable machine learning framework for binary classification, survival modelling, and regression analyses](https://mlsanity.com/paper/2501.04547/) (2025)
- [Survey for Categorising Explainable AI Studies Using Data Analysis Task Frameworks](https://mlsanity.com/paper/2507.10208/) (2025)

## Abstract (arXiv)

AutoML systems can speed up routine data science work and make machine learning available to those without expertise in statistics and computer science. These systems have gained traction in enterprise settings where pools of skilled data workers are limited. In this study, we conduct interviews with 29 individuals from organizations of different sizes to characterize how they currently use, or intend to use, AutoML systems in their data science work. Our investigation also captures how data visualization is used in conjunction with AutoML systems. Our findings identify three usage scenarios for AutoML that resulted in a framework summarizing the level of automation desired by data workers with different levels of expertise. We surfaced the tension between speed and human oversight and found that data visualization can do a poor job balancing the two. Our findings have implications for the design and implementation of human-in-the-loop visual analytics approaches.