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Robust Predictive Modeling Under Unseen Data Distribution Shifts: A Methodological Commentary

Hanyu Duan et al.

Information Systems Research2026https://doi.org/10.1287/isre.2022.0537article
FT50UTD24AJG 4*ABDC A*
Weight
0.50

Abstract

Predictive models are widely used to support decision making, yet they are typically built assuming that future data will follow the same distribution as the training data. In practice, data distributions often change in unseen ways, leading to poor model performance and reduced reliability. This methodological commentary highlights the risks of unseen data distribution shifts and shows how they are frequently overlooked in predictive modeling practice. Drawing on transfer learning, domain generalization, and distributionally robust optimization, we organize existing approaches to handling data shifts and illustrate how uncertainty-aware modeling can be implemented in practice. We conclude with actionable recommendations to guide the design, evaluation, and use of predictive models in uncertain data environments. Our work has implications for policy and practice related to trustworthy and responsible artificial intelligence (AI), predictive modeling, and AI risk management.

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https://doi.org/https://doi.org/10.1287/isre.2022.0537

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@article{hanyu2026,
  title        = {{Robust Predictive Modeling Under Unseen Data Distribution Shifts: A Methodological Commentary}},
  author       = {Hanyu Duan et al.},
  journal      = {Information Systems Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1287/isre.2022.0537},
}

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0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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