Model ChangeLists: Characterizing Updates to ML Models-Reference-Cited by-同舟云学术

Model ChangeLists: Characterizing Updates to ML Models

Published:2024-06-03 Issue: Volume:33 Page:2432-2453
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Container-title:The 2024 ACM Conference on Fairness, Accountability, and Transparency
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Author:

Eyuboglu Sabri¹^ORCID,Goel Karan²^ORCID,Desai Arjun²^ORCID,Chen Lingjiao³^ORCID,Monfort Mathew⁴^ORCID,Ré Chris²^ORCID,Zou James⁵^ORCID

Affiliation:

1. Computer Science, Stanford University, United States

2. Computer Science, Stanford University, USA

3. Stanford University, USA

4. Amazon Web Services, USA

5. Biomedical Data Science, Stanford University, USA

Funder

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3630106.3659047

Reference78 articles.

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2. Adaptive Importance Sampling: The past, the present, and the future

3. Joy Buolamwini and Timnit Gebru. 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency. PMLR, 77–91.

4. Lingjiao Chen, Tracy Cai, Matei Zaharia, and James Zou. 2021. Did the Model Change? Efficiently Assessing Machine Learning API Shifts. arXiv preprint arXiv:2107.14203 (2021).

5. Lingjiao Chen Zhihua Jin Sabri Eyuboglu Christopher Re Matei Zaharia and James Y Zou. [n. d.]. HAPI: A Large-scale Longitudinal Dataset of Commercial ML API Predictions. Advances in Neural Information Processing Systems Datasets and Benchmarks ([n. d.]).