ORES-Inspect: A technology probe for machine learning audits on enwiki
<|reference_start|>ORES-Inspect: A technology probe for machine learning audits on enwiki: 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.<|reference_end|>
@article{levonian2024ores-inspect:,
    title={ORES-Inspect: A technology probe for machine learning audits on enwiki},
    author={Zachary Levonian, Lauren Hagen, Lu Li, Jada Lilleboe, Solvejg
  Wastvedt, Aaron Halfaker, Loren Terveen},
    journal={arXiv preprint arXiv:2406.08453},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08453},
    primaryClass={cs.HC}
}