Machine Learning

{{Wikimedia engineering project information }}
 * name = Machine Learning
 * description = Wikimedia's Machine Learning Team
 * start = 2017-07-01
 * end =
 * group = Technology
 * team =
 * Staff
 * Chris Albon
 * Andy Craze
 * Kevin Bazira
 * Tobias Klausmann
 * Luca Toscano
 * Phabricator =
 * updates =
 * progress =
 * perennial = yes
 * projectpage = Machine Learning
 * backlog =
 * EPM = Chris Albon
 * display = {{{display|}}} }

Welcome to the homepage of the Wikimedia Foundation's Machine Learning team. The purpose of the Machine Learning team at the Wikimedia Foundation is to:


 * 1) Design, build, and maintain the foundation's machine learning infrastructure.
 * 2) Train, manage, and deploy production machine learning models:
 * 3) ... created by the Machine Learning team.
 * 4) ... created or requested by other Foundation teams.
 * 5) ... created or requested by Wikipedia communities.
 * 6) Develop best practices for applied ethical machine learning.

Team

 * Former
 * とある白い猫 (IEG)
 * Arthur Tilley (IEG)
 * He7d3r (IEG/Volunteer)
 * YuviPanda (Volunteer)
 * Sumit (Research Intern)
 * Ewitch (Research Intern)
 * Marius Hoch (Software Engineer)
 * Max Klein (Software Engineer)
 * Adam Wight (Software Engineer)
 * Natalia Timakova (Research Intern)
 * Amir Sarabadani (Software Engineer)
 * Nate TeBlunthuis (Research Intern)
 * James Hare (Associate Product Manager)
 * Aaron Halfaker (Principal Research Scientist & Team Lead)
 * Habeeb Shopeju (Software Engineering Intern)

Mission
There are two divergent conversations about artificial intelligences—in one, robots will save us from ourselves, and in the other, they destroy us. AI has great potential to help our projects scale by reducing the work that our editors need to do and enhancing the value of our content to readers, but AIs also have the potential to perpetuate biases and silence voices in novel and insidious ways. Imagine a world where AIs are powerful, open, accessible, audit-able tools that Wikimedians use to make their work easier. We develop and maintain AI services (like ) and the related technologies as a means to unlocking that future. We're an experimental, research focused, community supported, AI-as-a-service team. Our work focuses on balancing efficiency and accuracy with transparency, ethics, and fairness.


 * 1) We deliver realtime advanced machine prediction technologies in an easy-to-use format via a web service
 * 2) We focus on supporting all wiki communities who will collaborate with us
 * 3) We develop strategies and technological support for identifying and mitigating hidden biases
 * 4) We publish, communicate, and promote what we've learned so that others can benefit

Projects

 * ORES -- Machine learning prediction as a web service (see the list of tools that use ORES)
 * m:Wiki labels -- Training interface where Wikipedians teach machines how to perform important tasks
 * revscoring -- A machine prediction "scoring" framework for building prediction models used by ORES
 * JADE -- Robust false-positive and feedback gathering system, to allow human refutation and review of ORES scoring.



Research collaborations
We are working in collaboration with the following research projects studying ORES and the use of AIs in Wikipedia.


 * Research:Applying Value-Sensitive Algorithm Design to ORES
 * Research:Exploring systematic bias in ORES

Contact us
We're an open project team. There are many ways you can get in contact with us.
 * Email: calbon@wikimedia.org
 * Mailing list: [//lists.wikimedia.org/mailman/listinfo/ai ai@lists.wikimedia.org]
 * IRC:
 * Phabricator: [//phabricator.wikimedia.org/tag/machine_learning_team/ #Machine-Learning-Team]