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

TL;DR

ORES-Inspect is an open-source web tool designed to help Wikipedia editors audit and understand the performance of ML models like ORES, facilitating research into how users evaluate AI systems in collaborative environments.

Contribution

The paper introduces ORES-Inspect, a novel interface and research tool for auditing Wikipedia's ML models, addressing challenges in transparency and stakeholder engagement.

Findings

Enables editors to assess ML model performance effectively.

Facilitates research on user perceptions of AI auditing.

Supports transparency in vandalism detection models.

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.