# Don't Push the Button! Exploring Data Leakage Risks in Machine Learning and Transfer Learning

arXiv: https://arxiv.org/abs/2401.13796 · HTML: https://mlsanity.com/paper/2401.13796/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 48 references that are in the map.
- First posted on arXiv 24 Jan 2024.

## Builds on (most cited first, 9 of 9)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Return of Frustratingly Easy Domain Adaptation](https://mlsanity.com/paper/1511.05547/) (2015)
- [Unmasking Clever Hans Predictors and Assessing What Machines Really Learn](https://mlsanity.com/paper/1902.10178/) (2019)
- [Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans](https://mlsanity.com/paper/2008.06388/) (2020)
- [Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges](https://mlsanity.com/paper/1905.04223/) (2019)
- [Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methods](https://mlsanity.com/paper/2212.08744/) (2022)
- [On The Effects Of Data Normalisation For Domain Adaptation On EEG Data](https://mlsanity.com/paper/2210.01081/) (2022)
- [Management of Machine Learning Lifecycle Artifacts: A Survey](https://mlsanity.com/paper/2210.11831/) (2022)
- [Data Leakage in Notebooks: Static Detection and Better Processes](https://mlsanity.com/paper/2209.03345/) (2022)

## Built on by (most cited first, 12 of 12)

- [L-CiteEval: Do Long-Context Models Truly Leverage Context for Responding?](https://mlsanity.com/paper/2410.02115/) (2024)
- [On the Evaluation Protocol of Gesture Recognition for UAV-based Rescue Operation based on Deep Learning: A Subject-Independence Perspective](https://mlsanity.com/paper/2602.17854/) (2026)
- [Improving Image Data Leakage Detection in Automotive Software](https://mlsanity.com/paper/2410.23312/) (2024)
- [IMPACTX: improving model performance by appropriately constraining the training with teacher explanations](https://mlsanity.com/paper/2502.12222/) (2025)
- [ML2B: Benchmarking LLMs on Cross-Lingual ML Pipeline Generation](https://mlsanity.com/paper/2509.22768/) (2025)
- [Don't stop me now: How Validation Criteria Affect Checkpoint Selection and Early Stopping](https://mlsanity.com/paper/2602.22107/) (2026)
- [Which Leakage Types Matter? A Quantitative Landscape Across 2,047 Benchmark Datasets](https://mlsanity.com/paper/2604.04199/) (2026)
- [Instance-Adaptive Parametrization for Amortized Variational Inference](https://mlsanity.com/paper/2604.06796/) (2026)
- [Can Large Language Models Detect Methodological Flaws? Evidence from Gesture Recognition for UAV-Based Rescue Operation Based on Deep Learning](https://mlsanity.com/paper/2604.14161/) (2026)
- [Leak-Free Cross-Validated Stacking with Per-Architecture Calibration for Sand-Boil Segmentation in Earthen Levees](https://mlsanity.com/paper/2607.25367/) (2026)
- [It's All in the Way You Say It: The Role of Information Representation in LLM-Based Glycemic-Event Prediction](https://mlsanity.com/paper/2609.08772/) (2026)
- [PRAISE: A Reproducible Workflow for Non-Programmer Scientists to Implement Published AI Models](https://mlsanity.com/paper/2610.16980/) (2026)

## Abstract (arXiv)

Machine Learning (ML) has revolutionized various domains, offering predictive capabilities in several areas. However, with the increasing accessibility of ML tools, many practitioners, lacking deep ML expertise, adopt a "push the button" approach, utilizing user-friendly interfaces without a thorough understanding of underlying algorithms. While this approach provides convenience, it raises concerns about the reliability of outcomes, leading to challenges such as incorrect performance evaluation. This paper addresses a critical issue in ML, known as data leakage, where unintended information contaminates the training data, impacting model performance evaluation. Users, due to a lack of understanding, may inadvertently overlook crucial steps, leading to optimistic performance estimates that may not hold in real-world scenarios. The discrepancy between evaluated and actual performance on new data is a significant concern. In particular, this paper categorizes data leakage in ML, discussing how certain conditions can propagate through the ML workflow. Furthermore, it explores the connection between data leakage and the specific task being addressed, investigates its occurrence in Transfer Learning, and compares standard inductive ML with transductive ML frameworks. The conclusion summarizes key findings, emphasizing the importance of addressing data leakage for robust and reliable ML applications.