# AutoML in The Wild: Obstacles, Workarounds, and Expectations

arXiv: https://arxiv.org/abs/2302.10827 · HTML: https://mlsanity.com/paper/2302.10827/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 95 references that are in the map.
- First posted on arXiv 21 Feb 2023.

## Builds on (most cited first, 27 of 27)

- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Stealing Machine Learning Models via Prediction APIs](https://mlsanity.com/paper/1609.02943/) (2016)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [Machine Learning Models that Remember Too Much](https://mlsanity.com/paper/1709.07886/) (2017)
- [KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA](https://mlsanity.com/paper/2012.11014/) (2020)
- [Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Making](https://mlsanity.com/paper/1902.02960/) (2019)
- [FLAML: A Fast and Lightweight AutoML Library](https://mlsanity.com/paper/1911.04706/) (2019)
- [Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI](https://mlsanity.com/paper/1909.02309/) (2019)
- [Towards Backward-Compatible Representation Learning](https://mlsanity.com/paper/2003.11942/) (2020)
- [AutoML to Date and Beyond: Challenges and Opportunities](https://mlsanity.com/paper/2010.10777/) (2020)
- [Augmenting Neural Networks with First-order Logic](https://mlsanity.com/paper/1906.06298/) (2019)
- [Stakeholders in Explainable AI](https://mlsanity.com/paper/1810.00184/) (2018)
- [Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems](https://mlsanity.com/paper/2001.06509/) (2020)
- [AutoDS: Towards Human-Centered Automation of Data Science](https://mlsanity.com/paper/2101.05273/) (2021)
- [AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinates](https://mlsanity.com/paper/1912.06723/) (2019)
- [Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects](https://mlsanity.com/paper/2002.03389/) (2020)
- [ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning](https://mlsanity.com/paper/1902.05009/) (2019)
- [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)
- [Learning Backward Compatible Embeddings](https://mlsanity.com/paper/2206.03040/) (2022)
- [Automated Machine Learning in Practice: State of the Art and Recent Results](https://mlsanity.com/paper/1907.08392/) (2019)
- [Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML](https://mlsanity.com/paper/2104.04375/) (2021)
- [On the Security Risks of AutoML](https://mlsanity.com/paper/2110.06018/) (2021)

## Built on by (most cited first, 8 of 8)

- [AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML](https://mlsanity.com/paper/2410.02958/) (2024)
- [Position: A Call to Action for a Human-Centered AutoML Paradigm](https://mlsanity.com/paper/2406.03348/) (2024)
- [Assessing the Use of AutoML for Data-Driven Software Engineering](https://mlsanity.com/paper/2307.10774/) (2023)
- [Trustworthy, responsible, ethical AI in manufacturing and supply chains: synthesis and emerging research questions](https://mlsanity.com/paper/2305.11581/) (2023)
- [Evolving machine learning workflows through interactive AutoML](https://mlsanity.com/paper/2402.18505/) (2024)
- [Human-Centered AI Product Prototyping with No-Code AutoML: Conceptual Framework, Potentials and Limitations](https://mlsanity.com/paper/2402.07933/) (2024)
- [Agentomics-ML: Autonomous Machine Learning Experimentation Agent for Genomic and Transcriptomic Data](https://mlsanity.com/paper/2506.05542/) (2025)
- [A Framework for Assessing AI Agent Decisions and Outcomes in AutoML Pipelines](https://mlsanity.com/paper/2602.22442/) (2026)

## Abstract (arXiv)

Automated machine learning (AutoML) is envisioned to make ML techniques accessible to ordinary users. Recent work has investigated the role of humans in enhancing AutoML functionality throughout a standard ML workflow. However, it is also critical to understand how users adopt existing AutoML solutions in complex, real-world settings from a holistic perspective. To fill this gap, this study conducted semi-structured interviews of AutoML users (N=19) focusing on understanding (1) the limitations of AutoML encountered by users in their real-world practices, (2) the strategies users adopt to cope with such limitations, and (3) how the limitations and workarounds impact their use of AutoML. Our findings reveal that users actively exercise user agency to overcome three major challenges arising from customizability, transparency, and privacy. Furthermore, users make cautious decisions about whether and how to apply AutoML on a case-by-case basis. Finally, we derive design implications for developing future AutoML solutions.