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T18. AI quality management, model monitoring, and audit trails

Research and standards for keeping a deployed model accountable over time.

  1. T18-12Structural parallelSince 2025

    Provenance Tracking in Large-Scale Machine Learning Systems

    Authors
    Gabriele Padovani, Valentine Anantharaj, Sandro Fiore
    Year
    2025
    Venue
    ICPP Workshops 2025

    Korean review of T18-12

  2. T18-2Structural parallel

    The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction

    Authors
    Eric Breck, Shanqing Cai, Eric Nielsen, Michael Salib, D. Sculley
    Year
    2017
    Venue
    IEEE Big Data

    Korean review of T18-2

  3. T18-3Structural parallel

    Data Validation for Machine Learning

    Authors
    Neoklis Polyzotis, Martin Zinkevich, Sudip Roy, Eric Breck, Steven Whang
    Year
    2019
    Venue
    MLSys(Proceedings of Machine Learning and Systems) 1

    Korean review of T18-3

  4. T18-6Structural parallel

    Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing

    Authors
    Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, Parker Barnes
    Year
    2020
    Venue
    ACM FAT* 2020

    Korean review of T18-6

  5. T18-9Structural parallel

    Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift

    Authors
    Stephan Rabanser, Stephan Günnemann, Zachary C. Lipton
    Year
    2019
    Venue
    NeurIPS 2019 (Advances in Neural Information Processing Systems 32)

    Korean review of T18-9

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