Submissions:2025/Tone Check: a case study in developing ML to support editors
This submission has been noted and is pending review for WikiConference North America 2025.
Title:
- Tone Check: a case study in developing ML to support editors
Type of session:
- Lecture (15-30 min)
Session theme(s):
- Credibility, Future of Wikipedia
Abstract:
In this session, we’ll talk about the development of Tone Check, a feature designed to help new Wikipedia editors identify and revise language in their edits that may violate community policies by being promotional, derogatory, or otherwise subjective. Tone Check continues to be built in partnership with volunteers, through sessions like this one. This project is part of an ongoing commitment to fostering a more welcoming editing experience and proactive moderation process by offering people feedback while they are editing - before changes are published. We will provide an overview of the end-to-end development process, including identifying contributor needs, model design and training, model evaluation and testing, and feature integration into user-facing workflows. While machine learning plays a central role in how the feature works, the session will focus on how Tone Check addresses real challenges in editing, and how collaboration with communities is shaping both its design and impact.
Author name(s):
Wikimedia username(s):
Affiliated organization(s):
- Wikimedia Foundation
Estimated length of session
- 30 minutes
Will you be presenting remotely?
- I will present in-person
Okay to livestream?
- Livestreaming is okay
Previously presented?
- Not officially but there is existing documentation and prior discussions for Tone Check: https://www.mediawiki.org/wiki/Edit_check/Tone_Check#History
Special requests:
- N/A