# Help the machine to help you: an evaluation in the wild of egocentric data cleaning via skeptical learning

arXiv: https://arxiv.org/abs/2510.23635 · HTML: https://mlsanity.com/paper/2510.23635/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 40 references that are in the map.
- First posted on arXiv 24 Oct 2025.

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

- [Interactive Label Cleaning with Example-based Explanations](https://mlsanity.com/paper/2106.03922/) (2021)
- [Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI](https://mlsanity.com/paper/2409.14160/) (2024)
- [Learning in the Wild with Incremental Skeptical Gaussian Processes](https://mlsanity.com/paper/2011.00928/) (2020)
- [Continual egocentric object recognition](https://mlsanity.com/paper/1912.05029/) (2019)
- [Online AutoML: An adaptive AutoML framework for online learning](https://mlsanity.com/paper/2201.09750/) (2022)
- [DiversityOne: A Multi-Country Smartphone Sensor Dataset for Everyday Life Behavior Modeling](https://mlsanity.com/paper/2502.03347/) (2025)
- [Lifelong Personal Context Recognition](https://mlsanity.com/paper/2205.10123/) (2022)
- [What Impacts the Quality of the User Answers when Asked about the Current Context?](https://mlsanity.com/paper/2405.04054/) (2024)
- [A methodology and a platform for high-quality rich personal data](https://mlsanity.com/paper/2501.16864/) (2025)

## Abstract (arXiv)

Any digital personal assistant, whether used to support task performance, answer questions, or manage work and daily life, including fitness schedules, requires high-quality annotations to function properly. However, user annotations, whether actively produced or inferred from context (e.g., data from smartphone sensors), are often subject to errors and noise. Previous research on Skeptical Learning (SKEL) addressed the issue of noisy labels by comparing offline active annotations with passive data, allowing for an evaluation of annotation accuracy. However, this evaluation did not include confirmation from end-users, the best judges of their own context. In this study, we evaluate SKEL's performance in real-world conditions with actual users who can refine the input labels based on their current perspectives and needs. The study involves university students using the iLog mobile application on their devices over a period of four weeks. The results highlight the challenges of finding the right balance between user effort and data quality, as well as the potential benefits of using SKEL, which include reduced annotation effort and improved quality of collected data.