mlsanityData to 7 Oct 2026

Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows

arXiv 2101.04834 ↗First posted 13 Jan 2021cs.HC cs.LGcited by 22

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Abstract (arXiv)

Efforts to make machine learning more widely accessible have led to a rapid increase in Auto-ML tools that aim to automate the process of training and deploying machine learning. To understand how Auto-ML tools are used in practice today, we performed a qualitative study with participants ranging from novice hobbyists to industry researchers who use Auto-ML tools. We present insights into the benefits and deficiencies of existing tools, as well as the respective roles of the human and automation in ML workflows. Finally, we discuss design implications for the future of Auto-ML tool development. We argue that instead of full automation being the ultimate goal of Auto-ML, designers of these tools should focus on supporting a partnership between the user and the Auto-ML tool. This means that a range of Auto-ML tools will need to be developed to support varying user goals such as simplicity, reproducibility, and reliability.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 14 of its 48 references that are in the map. First posted on arXiv 13 Jan 2021.

Builds on

14 of 48 references.

References · 14

  1. 2017Snorkel: Rapid Training Data Creation with Weak Supervisioncited by 370
  2. 2018Auto-Keras: An Efficient Neural Architecture Search Systemcited by 169
  3. 2018A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspectivecited by 111
  4. 2019Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AIcited by 79
  5. 2020Human Factors in Model Interpretability: Industry Practices, Challenges, and Needscited by 73
  6. 2016AdaNet: Adaptive Structural Learning of Artificial Neural Networkscited by 54
  7. 2020Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systemscited by 36
  8. 2019Ludwig: a type-based declarative deep learning toolboxcited by 24
  9. 2019AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinatescited by 24
  10. 2019ATMSeer: Increasing Transparency and Controllability in Automated Machine Learningcited by 23
  11. 2020PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelinescited by 19
  12. 2017Serverless Computing: Current Trends and Open Problemscited by 17
2 more
  1. 2018Helix: Holistic Optimization for Accelerating Iterative Machine Learningcited by 11
  2. 2020Demystifying a Dark Art: Understanding Real-World Machine Learning Model Developmentcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 5; 2023: 7; 2024: 3; 2025: 4; 2026: 1.

22 papers.

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  1. 2022Operationalizing Machine Learning: An Interview Studycited by 25
  2. 2024Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Surveycited by 19
  3. 2023Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoMLcited by 16
  4. 2022Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Workcited by 15
  5. 2022IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspectivecited by 15
  6. 2023Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"cited by 15
  7. 2024Position: A Call to Action for a Human-Centered AutoML Paradigmcited by 14
  8. 2021Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelinescited by 13
  9. 2023AutoML in The Wild: Obstacles, Workarounds, and Expectationscited by 8
  10. 2024Large Language Models Synergize with Automated Machine Learningcited by 8
  11. 2023Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytellingcited by 7
  12. 2022An Empirical Study on the Usage of Automated Machine Learning Toolscited by 4
10 more
  1. 2025HyperSHAP: Shapley Values and Interactions for Explaining Hyperparameter Optimizationcited by 4
  2. 2025RAG Without the Lag: Interactive Debugging for Retrieval-Augmented Generation Pipelinescited by 3
  3. 2021Human-Centered AI for Data Science: A Systematic Approachcited by 2
  4. 2023Assessing the Use of AutoML for Data-Driven Software Engineeringcited by 2
  5. 2025LoCoML: A Framework for Real-World ML Inference Pipelinescited by 2
  6. 2022Practitioner Motives to Use Different Hyperparameter Optimization Methodscited by 1
  7. 2025EDCA - An Evolutionary Data-Centric AutoML Framework for Efficient Pipelinescited by 1
  8. 2023Predicting delays in Indian lower courts using AutoML and Decision Forestscited by 0
  9. 2023Eliciting Model Steering Interactions from Users via Data and Visual Design Probescited by 0
  10. 2026Fairness Theatre: Evaluating Post-Hoc Fairness Interventions in Vendor-Controlled Early Warning Systemscited by 0