ORES: Lowering Barriers with Participatory Machine Learning in Wikipedia
Channel: Aaron Halfaker
Date: 2020-09-23

This is a 5 minute teaser presentation for a paper to be presented as part of CSCW 2020.  

Full citation:
Aaron Halfaker and R. Stuart Geiger. 2020. ORES: Lowering Barriers with Participatory Machine Learning in Wikipedia. Proc. ACM Hum.-Comput. Interact. 1, 1, Article 148 (October 2020), 37 pages. https://doi.org/10.1145/3415219

Abstrac
Algorithmic systems—from rule-based bots to machine learning classifiers—have a long history of supporting the essential work of content moderation and other curation work in peer production projects. From countervandalism to task routing, basic machine prediction has allowed open knowledge projects like Wikipedia to scale to the largest encyclopedia in the world, while maintaining quality and consistency. However,
conversations about how quality control should work and what role algorithms should play have generally been led by the expert engineers who have the skills and resources to develop and modify these complex
algorithmic systems. In this paper, we describe ORES: an algorithmic scoring service that supports real-time scoring of wiki edits using multiple independent classifiers trained on different datasets. ORES decouples
several activities that have typically all been performed by engineers: choosing or curating training data, building models to serve predictions, auditing predictions, and developing interfaces or automated agents that
act on those predictions. This meta-algorithmic system was designed to open up socio-technical conversations about algorithms in Wikipedia to a broader set of participants. In this paper, we discuss the theoretical
mechanisms of social change ORES enables and detail case studies in participatory machine learning around ORES from the 5 years since its deployment.

Key words: Wikipedia; Reflection; Machine learning; Transparency; Fairness; Algorithms; Governance