Exploring quality dimensions in trustworthy Machine Learning in the context of official statistics: model explainability and uncertainty quantification

Author:

Molladavoudi Saeid,Yung Wesley

Publisher

Springer Science and Business Media LLC

Subject

General Economics, Econometrics and Finance,General Social Sciences,Statistics and Probability

Reference59 articles.

1. Alvarez-Melis D, Jaakkola TS (2018) On the robustness of interpretability methods (presented at 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018), Stockholm, Sweden)

2. Angelopoulos AN, Bates S (2021) A gentle introduction to conformal prediction and distribution-free uncertainty quantification (arXiv:2107.07511)

3. Angelopoulos AN, Bates S, Fisch A, Lei L, Schuster T (2022) Conformal risk control (arXiv:2208.02814)

4. Angelopoulos AN, Bates S, Fannjiang C, Jordan MI, Zrnic T (2023) Prediction-powered inference (ArXiv:2301.09633)

5. Barber RF, Candès EJ, Ramdas A, Tibshirani RJ (2021) Predictive inference with the jackknife. Ann Stat 49(1):486–507. https://doi.org/10.1214/20-AOS1965

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1. Editorial issue 3 + 4, 2023;AStA Wirtschafts- und Sozialstatistisches Archiv;2023-12

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