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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. T18-11Candidate approachSince 2025

    Time to Retrain? Detecting Concept Drifts in Machine Learning Systems

    Authors
    Tri Minh Triet Pham, Karthikeyan Premkumar, Mohamed Naili, Jinqiu Yang
    Year
    2025
    Venue
    ICSE 2025

    Korean review of T18-11

  3. T18-1Similar problem

    Hidden Technical Debt in Machine Learning Systems

    Authors
    D. Sculley and 9 others (Google)
    Year
    2015
    Venue
    NeurIPS(NIPS) 28

    Korean review of T18-1

  4. 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

  5. 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

  6. 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

  7. T18-7Similar problem

    A Survey on Concept Drift Adaptation

    Authors
    João Gama, Indrė Žliobaitė, Albert Bifet, Mykola Pechenizkiy, Abdelhamid Bouchachia
    Year
    2014
    Venue
    ACM Computing Surveys 46(4), 1-37

    Korean review of T18-7

  8. T18-8Similar problem

    Learning under Concept Drift: A Review

    Authors
    Jie Lu, Anjin Liu, Fan Dong, Feng Gu, João Gama, Guangquan Zhang
    Year
    2019
    Venue
    IEEE Transactions on Knowledge and Data Engineering 31(12), 2346-2363

    Korean review of T18-8

  9. 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

  10. T18-10Similar problem

    Operationalizing Machine Learning: An Interview Study

    Authors
    Shreya Shankar, Rolando Garcia, Joseph M. Hellerstein, Aditya G. Parameswaran
    Year
    2022
    Venue
    arXiv (UC Berkeley)

    Korean review of T18-10

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