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arXiv · 2406.08453

ORES-Inspect: A technology probe for machine learning audits on enwiki

Zachary Levonian·Lauren Hagen·Lu Li·Jada Lilleboe·Solvejg Wastvedt·Aaron Halfaker·Loren Terveen
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Abstract

Auditing the machine learning (ML) models used on Wikipedia is important for ensuring that vandalism-detection processes remain fair and effective. However, conducting audits is challenging because stakeholders have diverse priorities and assembling evidence for a model's [in]efficacy is technically complex. We designed an interface to enable editors to learn about and audit the performance of the ORES edit quality model. ORES-Inspect is an open-source web tool and a provocative technology probe for researching how editors think about auditing the many ML models used on Wikipedia. We describe the design of ORES-Inspect and our plans for further research with this system.

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Zachary Levonian, Lauren Hagen, Lu Li, Jada Lilleboe, Solvejg Wastvedt, Aaron Halfaker, Loren Terveen. 2024-06-12. ORES-Inspect: A technology probe for machine learning audits on enwiki. https://arxiv.org/abs/2406.08453

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